JANZZ.technology – The Podcast

Laura: Hi everyone, welcome back and Happy New Year! This 2025 we will continue diving into the world of labor markets, technology, and innovation. I’m your host, Laura Flamarich, and as always, I’m thrilled to explore today’s topic—a fascinating intersection of family, education, and careers in the modern workforce.

To frame our discussion, I’d like to mention our last research paper titled Shaping the Future: The Influence of Family on Children’s Education and Career Choices in Indonesia. This study dives into how deeply family dynamics, social norms, and education systems shape career paths.
Joining me to unpack these themes is Hesti Aryani. Hesti is not just our Business Development Manager for Indonesia at JANZZ but also an expert on labor market information systems that bridge the gap between job seekers and employers. And, like me, she’s also a mom raising a daughter. Hesti, it’s such a pleasure to have you here!

Hesti: Thank you, Laura. It’s wonderful to be here, I’m excited about this conversation both as a professional and as a parent.

Laura: That’s such a great starting point! Can I ask you something personal?

Hesti: Of course, go ahead.

Laura: As a mom, how does having a daughter influence the way you approach your work? Especially when it comes to empowering young people?

Hesti: That is a good question…Having a daughter has shaped the way I think about my work.

Laura: How?

Hesti: Well, it gives me a very personal stake in the future we’re building. I think about what kind of opportunities I want her to have.

Laura: For instance?

Hesti: For instance, opportunities that aren’t limited by societal expectations or traditional norms… Giving her the freedom to be anything she wants.

Laura: That’s inspiring, Hesti. How does family dynamics and parental influence shape the future of Indonesian children?

Hesti: In Indonesia, parental influence often begins very early. Many parents see education as a direct path to economic stability. For example, families might encourage their children to pursue careers in the fields that they perceive as stable—such as civil service, government officials, teaching, or law enforcement—sometimes without considering the child’s interests or potentials, or by considering the changing job market.

Laura: I see… Hesti, seeing how far you have come, how was your own experience growing up in Indonesia?

Hesti: Well, I grew up in a society where traditional Javanese culture was still deeply rooted, much like what is described in the paper, along with all those social expectations that women were more into domestic roles and were expected to be married before the age of 25, should not pursue too high education because we women would eventually end up in the kitchen and serve the family. Something like that, you know. But, despite coming from a religious family in Pekalongan, Central Java, I was fortunate enough that my parents allowed me the freedom to shape my own path. Although they didn’t pursue higher education, they were incredibly forward-thinking and open-minded.

Laura: This is wonderful. What did they do for a living?

Hesti: My father was a local businessman, a hardworking, ambitious, and deeply committed person. He encouraged me to excel in my studies, particularly in learning English—a language he didn’t understand himself. I believe he saw me as his last hope and a chance to invest in education, hoping it would lead to a higher level of achievement and, obviously, a better life in the future. So, as the youngest in the family, my childhood was filled with extracurricular activities that kept me busy.

Laura: That’s incredible. It sounds like your father had a clear vision for your future, even without fully understanding the tools he encouraged you to pursue. And what about your mom?

Hesti: My mom was a teacher at a local primary school, and I remember she used to take me to her classrooms because I couldn’t be left alone unattended at home. So I’d sit quietly in the corner, just watching her teaching. Looking back, I think that really inspired me—I wanted to be like her. And eventually, I did become a language teacher at some universities for over a decade, which was such a rewarding experience.

Laura: That’s such a beautiful memory, Hesti. It’s no wonder those early experiences had such a profound impact on your career path.

Hesti: Yeah, indeed. But, you know, life has its twists and turns, and along the way, I switched my career out of academia into a corporate life, which is a completely different world to where it all started.

Laura: What a journey. What motivated you to step away from academia and venture into the corporate world?

Hesti: To be honest it left me no choice. Ha ha ha … In academia, in my case, it was just neither a fixed-term nor a permanent contract being offered on the table, which made me obviously anxious about my future with all of those uncertainties. So I worked partially in the education business as a consultant and specialising in recruiting and training teachers back in Indonesia. Then, I had a short-term teaching position here in Zurich right before the pandemic, and eventually I challenged myself to try and learn something new by joining the IT industry, which is what I’ve been doing now at JANZZ.technology.

Laura: Very good, and I’m so glad you did! Could you tell us more about your current role and its connection to your background?

Hesti: My current work focuses on developing semantic-based AI for the labor market. In a way, it feels like a mixture of all my past experiences—my education background as a linguist and teacher, combined with my years of working in managerial roles within the labor market. So, it’s been a fascinating journey!

Laura: Absolutely! It sounds like your journey is a perfect example of how important it is to stay adaptable.

Hesti: Exactly. I believe it highlights the importance of unlocking one of the most critical skills of this century: agility and adaptability. In today’s ever-changing world, those qualities are more essential than ever.

Laura: So true. Hesti, I know you still give some lectures from time to time. Given the current situation that students face. How do you encourage them in finding opportunities in today’s workforce?

Hesti: The job market in Indonesia is challenging right now, the unemployment rate is still high, with limited job vacancies and a lot of competition. However, I always encourage my students to stay adaptive. The labor market constantly evolves, so I advise them to stay open to further education, explore new fields, and develop diverse skills. It’s about helping them discover opportunities that truly align with their potential and preparing them to thrive in an ever-changing environment.

Laura: That’s such an important balance to strike.

Hesti: It really is. It’s about creating an environment where the next generation can explore, thrive, and succeed on their own terms.

Laura: That’s amazing. How does JANZZ.technology align with and support this mission?

Hesti: As we are now living in a digitalized world, we should not forget how technology plays a significant role in shaping our future. Our labor market tools help provide data-driven insights to show society like families, students and job seekers the full spectrum of opportunities available, including those in emerging sectors like technology, digital marketing, and creative industries.

Laura: That’s fascinating. It highlights how societal shifts are paving the way for unconventional career paths for Generation Z. However, do you find that families are resistant to embracing these exciting new opportunities?

Hesti: Definitely. Many families still view these newer careers with skepticism, often because they don’t fully understand them. For instance, careers like “content creator” or “digital marketer” are not seen as prestigious as traditional roles like doctors or civil servants.

Laura: I see.

Hesti: And there’s more, remote work, for example, is very often misunderstood.

Laura: Really…?

Hesti: Oh, yes. In the village, for example. Many families struggle to understand how someone can be working in front of a computer all day long without leaving the house…to them, working from home is perceived as unemployment, rather than a legitimate career option. It’s hard to understand that a work can be done remotely just in front of your screen. That’s why part of our work involves educating not just job seekers but also their families about how the labor market today is evolving. AI tools can map how skills from traditional education are able to translate into these modern roles, which helps alleviate family concerns.

Laura: That’s insightful. Speaking about rural areas, how does JANZZ.technology address migration?

Hesti: Migration is a big thing in Indonesia. Many young people leave rural areas because they perceive cities as the only places with opportunities. But this often leads to underemployment or mismatch because the urban job market can’t absorb everyone. Our AI systems are designed to highlight local job opportunities by connecting job seekers with nearby employers who might otherwise be unaware of their skills.

Laura: That’s a great approach. On a slightly different note, does gender play a role in shaping career trajectories?

Hesti: Gender plays a huge role. Traditionally, women in Indonesia have faced societal expectations to prioritize family over career, especially in rural areas. This often limits their participation in the labor market or confines them to specific roles. In my case, for example, it was not well received that I was going abroad.

Laura: How come?

Hesti: Well, pretty classic reasons—because I am a woman. They were afraid of having me work in certain foreign environments. There’s also a lot of stigma around women working abroad, with families often worried about the dangers they might face. In Indonesia, there’s still a cultural stereotype that limits opportunities for women, especially when it comes to being migrant workers. This includes concerns about potential risks of trafficking, exploitation, and other issues that need to be addressed.

Laura: Mmm. Do you know what can be done in order to promote equal opportunities?

Hesti: Well, at JANZZ.technology, we are actively working to break stereotypes by offering tools that promote transparency and equal employment opportunities, such as anonymous application processes.This helps reduce bias and ensures that candidates are judged purely on their skills and qualifications.

Laura: Unfortunately, women often face unconscious biases in hiring. But it’s not just about getting the job, right? It’s also about making workplaces more adaptable to women’s unique needs, especially for working mothers.

Hesti: Exactly, Laura. At JANZZ, we also support women and working mothers by facilitating flexibility in working hours, creating a company culture that values their contributions while allowing them to balance their professional and personal lives. It’s something I deeply appreciate as a mother myself, and it’s a value I hope more companies in Indonesia will adopt.

Laura: Supporting women is not just good for the women themselves, right?

Hesti: Right, women’s participation in the labor market is crucial, especially in developing countries. If women are given equal access to flexible, meaningful work, it could significantly boost the economy while empowering families and communities. This is a vision I’m passionate about and I want to bring to Indonesia—creating systems and opportunities that make it easier for women to thrive in their careers without sacrificing their other responsibilities.

Laura: Well said. Hesti, with AI being such a transformative tool, how do you ensure it remains inclusive and doesn’t exacerbate existing inequalities?

Hesti: Inclusivity is a key principle in our AI design. One way we achieve this is by integrating local context into our systems. We also ensure that our algorithms don’t favor certain demographics, religion, gender, or job seekers with special needs, over others by using diverse datasets to train the models. The goal is to create a system that works for everyone.

Laura: That’s crucial. How do you think AI will continue to shape the future?

Hesti: AI has immense potential to transform the future of work in Indonesia, especially by making the labor market system more inclusive and accessible. It can help bridge gaps in education, identify skills gaps, and match job seekers with the right opportunities based on their abilities, not just their credentials. With AI, we can personalize career pathways, allowing individuals to navigate the ever-changing job market more efficiently. Additionally, AI will play a key role in shaping industries like technology, agriculture, and healthcare, unlocking new career possibilities and driving economic growth. The challenge, of course, is ensuring that AI tools are accessible and tailored to local contexts, and most importantly, that are fully compliant to AI ethics.

Laura: That’s such an inspiring vision, Hesti. Thank you for sharing your insights and your incredible work. It’s clear that your efforts are shaping a brighter future not just for Indonesia, but for all developing nations navigating the challenges of the digital economy.

Hesti: Thank you, Laura. It’s been a pleasure to share this space with you. Here’s to building a future where next generations can dream big and achieve even bigger.

Laura: Well said. Thanks again for joining us, and to all our listeners, if you are interested in reading our last research paper, Shaping the Future: The Influence of Family on Children’s Education and Career Choices in Indonesia, you will find the link in the podcast description. Thank you for listening, and goodbye.

Laura: Hi, everyone! Welcome to a new episode, Securing the Future. I’m your host, Laura Flamarich, and today, we’re talking with Elizabeth D’Alessandro, the Chief Information Security Officer at JANZZ.technology. Elizabeth, thanks for being here!

Elizabeth: Thank you, Laura! I’m really glad to be here. Security is such an important topic, and I’m happy to share whatever insights I can. I’m looking forward to the conversation!

Laura: Let’s start with the basics. The family of ISO 27000 sounds complex. For listeners who might be unfamiliar, can you break it down?

Elizabeth: Absolutely! The ISO 27000 family is a series of standards for managing information security. At its core, it’s about helping organizations protect their sensitive information—whether that’s employee records, customer data, or intellectual property.

The center piece is ISO 27001. Think of it as the main guidebook. It outlines how to build an Information Security Management System, which is essentially a framework for managing security risks.

Then you’ve got the rest of the family, which dives into more specific areas:

For example ISO 27002 gives detailed security controls that can be implemented, like access management, encryption, etc.

And ISO 27701 includes requirements for managing Personally Identifiable Information (PII), making it great for GDPR alignment.

I like to describe it as a toolbox. Depending on your organization’s needs, you pick the tools that fit best.

Laura: Aaaah, I see! That clears up a bit, Elizabeth. Thanks for breaking it down.

Elizabeth: Haha, happy to be your ISO interpreter, Laura! It’s a bit like assembling a security puzzle with all the right pieces.

Laura: Right! Can you tell us a bit about where ISO 27000 comes from?

Elizabeth: The ISO 27000 story begins Back in the 1990s, when computers and the internet were becoming essential tools and there were some hacking attacks and viruses, people realized there needed to be a clear way for companies to protect sensitive data—whether it was customer details, business plans, or even employee records.

The first step was taken in the UK, with something called BS 7799. It was like an early guidebook for how to handle security. People liked it so much that it went global, and ISO updated it and turned it into ISO 27001.lkndfldsglsdlgldgsl
From there, it expanded into a full family of standards, covering everything from risk management to cloud security. The idea was simple—security isn’t a one-size-fits-all approach, so they created a suite of tools to handle different challenges. Over the years, it’s been revised to keep up with new threats and technologies.

Laura: Now, what happens if a company doesn’t have robust security measures in place?

Elizabeth: The risks can be catastrophic, both financially and operationally.

Laura: Could you share some real-world examples of what happens when companies don’t take security seriously?

Elizabeth: Sure. One example that stands out is the Equifax data breach in 2017. Hackers stole the personal information of over 140 million people.

Laura: Over 140 million people! That’s equivalent to about 16 times the population of Switzerland.

Elizabeth: Exactly, Laura! It’s mind-boggling, isn’t it? Imagine the entire country being affected more than 16 times over! It’s not just numbers—it’s millions of real people, their identities, and their trust.

Laura: What was the issue?

Elizabeth: It happened because of a known software vulnerability that wasn’t patched in time. Hackers exploited it and accessed sensitive data, including credit card details.

Laura: Ooh… That’s scary!

Elizabeth: There were massive fines, lawsuits, and a long-term hit to customer trust. It’s a classic example of how a small vulnerability can lead to disaster.

Laura: It’s shocking how often basic security measures are overlooked.

Elizabeth: Absolutely. Another big one is the ransomware attack on the Colonial Pipeline in 2021. A single compromised password allowed attackers to deploy ransomware, forcing the company to shut down operations for six days, leading to widespread fuel shortages, panic buying, and a major disruption in supply chains.

Laura: Apologise my ignorance but what exactly is ransomware?

Elizabeth: No worries, Laura! In simple terms, ransomware is a type of malicious software—kind of like a digital hostage situation. It locks up a company’s data or systems, making them completely unusable. The hackers then demand money to unlock everything, usually in cryptocurrency.

Laura: Ah, that sounds much clearer! I imagine robust security measures could’ve helped there?

Elizabeth: Yes, It’s about anticipating vulnerabilities and being proactive rather than reactive.

Laura: It’s incredible… all because of one weak link, and it can disrupt an entire system.

Elizabeth: It really is. Should I continue? There are many examples, it has also happened here in Switzerland.

Laura: Oh, please, go ahead!

Elizabeth: A recent example of a significant cybersecurity incident in Switzerland involved a company that provides software to Swiss government bodies, including the federal police and the customs department. It was also a ransomware attack. This incident raised concerns because it potentially affected critical government operations.

Another example is British Airways, which was fined £20 million under GDPR for failing to secure customer payment data. Hackers inserted malicious scripts into their website and app, redirecting payments to a fraudulent server.

Laura: And wasn’t there a major attack on Sony Pictures a while ago?

Elizabeth: Yes, hackers also breached Sony’s systems, stealing sensitive emails, employee data, and even unreleased films.

Laura: So, Elizabeth, JANZZ.technology got recently certified with the standard ISO 27001. Why was this so important?

Elizabeth: At JANZZ.technology, we deal with massive amounts of data—everything from multilingual job descriptions to sensitive skills data. As a company working with governments and private organizations worldwide, we want to show that we’re serious about security.

Achieving ISO 27001 certification means that JANZZ has established, implemented, and maintained a robust information security management system. It’s like getting a gold star in data protection—it demonstrates that the company takes security seriously and has a structured approach to protecting sensitive information.

It also shows that we are committed to continuous improvement, because this certification isn’t a one-time deal; companies are audited regularly to ensure they keep up with changing threats and maintain compliance.

Laura: Can you give a practical example of how these controls are used at JANZZ?

Elizabeth: Sure! Let’s talk about encryption. One of the controls in 27002 focuses on encrypting data at rest and in transit. At JANZZ, we encrypt job data and personal information to prevent unauthorized access, even if someone tries to intercept the data during transmission.

Another example is user access management. We limit system access to only those who need it for their role. This mitigates risks like accidental data leaks.

Laura: Have these measures ever been put to the test?

Elizabeth: Definitely. One incident comes to mind where a client organization was targeted by phishing emails. Because of our controls, including email filtering and multi-factor authentication, we caught the attack early and prevented the compromise of sensitive credentials.

Laura: It’s great to hear that proactive measures like those can make such a difference!

Elizabeth: We have transitioned our main communication channels to safer and more private tools for communication and collaboration. These tools are managed within our own secure networks, ensuring enhanced protection when interacting with both our customers and colleagues. This way we prevent most or many such threats in the future…

Laura: That’s impressive. It really highlights how preparation pays off.

Elizabeth: Exactly. Cybersecurity is a lot like health. It’s better to prevent the illness than to cure it.

Laura: I like that analogy. Before we wrap up, Elizabeth, what advice would you give to other organizations?

Elizabeth: Start by conducting a risk assessment. Understand what’s at stake and prioritize your controls based on the highest risks. And don’t underestimate the importance of employee training. Most breaches start with human error, so awareness is key.

Laura: That’s such valuable advice. Elizabeth, thank you for sharing your insights and showing us how standards like ISO 27000 can make a real difference.

Elizabeth: My pleasure, Laura. Thanks for having me!

Laura: That’s a wrap for today. Remember, security isn’t just about technology; it’s about creating a culture of awareness. Thank you for listening and goodbye!

Laura: Hi everyone, welcome to a new episode! I’m Laura Flamarich, host of this podcast and today we wanted to dive deep into one of the most transformative forces in the labour market: Artificial Intelligence.

AI has made its way into almost every single industry on the market. From healthcare to finance, passing by manufacturing, education, and many less known things like beekeeping, winemaking or even personal fashion styling!

It’s transforming the way we work, the types of jobs available, and even how we think about our careers. But what does that mean for all of us? How is AI really impacting today’s labour market, and how can we prepare for the changes ahead? To break it down, I’m excited to welcome our guest expert, Stefan Winzenried, CEO and founder of JANZZ technology, who has been working with AI for a while and will help us understand how AI and automation are shaping the future

Stefan: Hi Laura, thanks for having me. I’m excited to explore this topic. It’s one of those conversations that swings between extremes—either doom and gloom or idealistic visions of a perfect future. The truth is, it’s more nuanced than that.

Laura: Exactly. We’ve heard people talking about how AI could either destroy jobs or create more meaningful, less repetitive work. So, where do you stand on that?

Stefan: Well, let’s start by defining what we mean by AI-driven digital transformation. It’s essentially about automating tasks that humans can’t or don’t want to do. This isn’t new. Think back to the industrial revolutions—each time, people feared job loss. Yet, businesses have always driven these changes to stay competitive and cut costs.

Laura: Right, like the introduction of steam-powered machines in factories. But what’s different now? Previous technologies were also seen as a threat in the beginning, right?

Stefan: Yes, but past revolutions mainly replaced physical demanding or repetitive labor, AI is now replacing more complex, higher-skilled jobs. We’re talking about AI that can diagnose cancer better than some doctors, or algorithms that help create digital art and write compelling content… and this goes beyond manual labor.

Laura: Right, but hasn’t technology always led to new kinds of work?

Stefan: That’s a common argument, and it’s partly true. For instance, when cars replaced horses, people worried about jobs in transportation. But new industries, like roadside diners and motels, emerged. The question is whether this AI revolution will follow the same pattern. Will we create enough new jobs, or will we end up with a surplus of workers who can’t transition easily into these new roles?

Laura: Some reports suggest that AI might create more jobs than it destroys, but others predict significant losses, especially in industries that rely on repetitive tasks. So what’s the reality?

Stefan: The reality is, no one can say for sure. Studies show conflicting numbers. Even if we break even on job numbers, we might face skill mismatches. Some industries will have talent shortages, while others will see unemployment. It’s not just about numbers; it’s about the kinds of jobs that are created.

Laura: Right —there’s this claim that AI will only destroy repetitive jobs, allowing workers to focus on “higher-value” tasks. What do you think about that?

Stefan: I’m skeptical. It sounds good in theory. But not everyone has the skills or desire to shift into those roles. For instance, a factory worker might not find the same satisfaction in programming a machine. And these so-called “higher-value” jobs may not be accessible to everyone.

Laura: Of course, we should consider that some people find satisfaction in what others might call mundane work. So, this one-size-fits-all argument doesn’t hold up. And there’s another claim that we can just upskill workers. Is that the magic solution?

Stefan: Upskilling sounds great, but it’s not that simple. First, we can’t predict the future of work accurately. AI is creating jobs we didn’t even imagine a few years ago, and it’s hard to train for jobs we don’t know exist yet. Plus, what happens to people who can’t access training? In the US and EU, up to 20% of adults have low literacy levels, making it hard for them to reskill. And then there’s the issue of the working poor—many don’t have the time or resources to learn new skills while holding down jobs.

Laura: But what about businesses? Shouldn’t they invest in training their workers as an investment in the future?

Stefan: In an ideal world, yes. But businesses are profit-driven. If replacing workers with AI is cheaper and more efficient, many will go that route rather than reskill workers. We’re already seeing this in places like call centers—AI chatbots are handling thousands of customer queries with fewer humans needed. From a business standpoint, it’s a no-brainer. The competition will push them in that direction.

Laura: True. AI it’s also about efficiency. But what does that mean for human work in the long term?

Stefan: Well, this is where things get philosophical. If we end up working less because of AI, what does that mean for our sense of purpose? Work has always been a fundamental part of human life. If AI takes over a lot of it, will we lose something important? And will society distribute the benefits of AI fairly, or will it deepen inequality?

Laura: Those are big questions—AI could either liberate us from work, or it could exacerbate wealth gaps if the benefits aren’t shared. I guess the key takeaway here is that we need to prepare for both possibilities, right?

Stefan: Absolutely. We can’t assume that everything will sort itself out, as it did in past revolutions. We need realistic strategies—whether that’s retraining, tax policies on AI, or better social safety nets—to make sure the future of work benefits everyone, not just a select few.

Laura: Exactly, and that brings up one important question: what actions can we take as individuals?

Stefan: On an individual level, I think it’s about being proactive. We can’t rely on companies or governments to solve everything for us. Staying adaptable is key—whether that’s learning new skills, embracing technology, or even developing strong interpersonal skills that are harder to automate. But it’s also about recognizing that individual resilience alone isn’t enough. We need structures in place to support people, like affordable retraining programs and career transition resources.

Laura: Right, but what about those jobs that are crucial to society but often undesirable or even underpaid? You know, things like waste management, sanitation, certain types of caregiving, even some roles in agriculture or in the tourism industry—jobs that many rely on yet wouldn’t choose to do themselves.

Stefan: Yes, those jobs are the backbone of society, and they often don’t get the respect or compensation they deserve. One thing we’re seeing is an increased interest in automation to handle some of those roles. For example, there’s more investment in robotics for dangerous or physically demanding work in fields like construction or waste management. But we’re far from fully automating all those roles, and honestly, some of them might always require a human touch—like caregiving.

Laura: So in cases where automation isn’t feasible or desirable, what could be done to make those jobs more appealing?

Stefan: Good question. Part of it comes down to valuing those roles more openly—both in terms of pay and social recognition. We could look at things like wage subsidies, improving working conditions, or even offering tax incentives to make these essential jobs more attractive. There’s also a cultural shift that needs to happen; as a society, we should be recognizing and celebrating the people who take on these roles because they’re fundamental.

Laura: But do you think these changes are happening fast enough, or even happening at all?

Stefan: I’d say society is still catching up with the rapid pace of technology, and it’s a gap we can’t ignore. Despite advancements, we still rely heavily on manual jobs and skilled trades, like those of handymen and other essential roles that technology currently can’t replace. That’s one of the reasons why, at JANZZ.technology, we’ve been analysing labour markets globally for years. We work on forecasting the unique workforce needs of each locality to provide actionable insights for the future.

Ensuring a balanced transition will require more than just technological solutions; it’ll need a coordinated mix of policy innovation, commitment from the private sector, and heightened public awareness to build a workforce strategy where no one is left behind.

Laura: Well, that’s a hopeful note to end on. Thanks, Stefan. I think we’ve unpacked some crucial points today. AI is already transforming work, but it’s up to us to shape the outcomes. Thank you for listening and goodbye.

Laura: In this episode we talk about why we do need new KPIs to describe labor markets more effectively.

Why do we need new Key Performance Indicators—also known as KPIs—to more effectively describe labor markets?

To help us unpack this topic, I’m joined by a very special guest—Stefan Winzenried, CEO and founder of JANZZ.technology, and an expert in labor market analytics. Welcome to the studio!

Stefan: Thanks for having me!

Laura: Let’s start with the basics: Why have traditional KPIs, like unemployment rates and labor market participation, been the main focus for so long?

Stefan: Well, take the unemployment rate as an example. It’s been a central metric for decades because it’s relatively simple to measure, easy to communicate, and it has a direct impact on people’s lives. When unemployment goes up, governments and businesses can respond. The same goes for labor market participation, which shows how many people are working or actively looking for work.

But the problem with these traditional KPIs is that they don’t give us the full picture.

Laura: How so?

Stefan: Well, for instance, a 4% unemployment rate might look great on the surface, but it doesn’t tell you if the jobs people are getting actually match their skills, or if they’re underemployed, like working part-time but wanting full-time hours. It also doesn’t show whether people are earning a living wage…

Laura: So in a way, these metrics are more like an “on-off switch”—either you’re employed or you’re not—without addressing the quality or suitability of the job. Is that right?

Stefan: Exactly. And that’s where the concept of “labor market mismatch” comes in. It’s not just about whether people are working; it’s about whether they’re in the right jobs based on their skills, education, and experience.

Laura: Right.

Stefan: Think about it: in today’s world, the economy is shifting rapidly. We have automation, AI, global trade, and a growing demand for digital skills. These factors are creating more complexity in labor markets than ever before.

Laura: So, we are saying that even though someone might be employed, they could, for instance, still be in a role that doesn’t fully leverage their skills.

Stefan: Exactly. Let’s say you’re a highly educated software engineer working in a retail store. You’re technically employed, so you wouldn’t show up as unemployed in government stats. But clearly, you’re not in a role that utilizes your full skill set. This is a form of labor market mismatch—and it’s becoming a bigger issue in both developed and developing countries.

Laura: We agree that labor market mismatch is a very relevant KPI, especially with technology driving changes in the workforce. How would tracking mismatch data help both businesses and policymakers make better decisions?

Stefan: That’s a great question. When we measure mismatch, we’re essentially identifying gaps in the labor market—whether that’s skills gaps, geographic mismatches, or even sectoral mismatches where industries struggle to find the right talent.

For policymakers, tracking mismatch data helps in tailoring education and training programs to meet real-time labor market needs. For instance, if we know that there’s a shortage of workers with data science skills in a certain region, governments can adjust funding for universities or vocational programs accordingly.

Businesses can also benefit by identifying where to focus their hiring efforts or invest in employee reskilling to reduce turnover and improve productivity.

Laura: Interesting! So, instead of focusing purely on getting people into jobs, we’re focusing on getting people into the right jobs.

Stefan: Exactly. Labor market mismatch data also helps to forecast future trends. If you can identify mismatches now, you can predict where shortages will occur and plan accordingly. This leads to a more resilient labor market and reduces inefficiencies that come from having the wrong people in the wrong roles.

Laura: It makes sense, especially when considering the challenges brought by technological change, globalization, and even the pandemic.

Stefan: The mismatch is very big… and the main issue with most of the unfilled positions we see is that candidates either don’t want the jobs or are overqualified.

Laura: Necessary jobs are not attractive because of low wages, bad working conditions, and disloyal employers. We have written a white paper on this topic, which I’ll link below in the podcast description. MIN 4:43.

Stefan: Exactly, and that’s why it’s crucial to look beyond just the unemployment rate or labor participation numbers. They don’t help us improve the situation. We need to effectively understand and describe labor markets.

Laura: How difficult is it to measure mismatch compared to traditional KPIs like unemployment?

Stefan: It can be complex, but that’s where technology, like what we develop at JANZZ, plays a critical role. With advanced data analytics, AI, and machine learning, we can now assess vast datasets from job postings, employee profiles, and even social networks to analyze patterns of skills and occupations. We’re moving beyond surveys and self-reporting, which were traditionally used to gather labor market data.

By leveraging this technology, we can track in real-time where mismatches are occurring—whether that’s in a specific industry or geographic area—and provide actionable insights to employers, educational institutions, and governments.

Laura: That’s a major shift, especially for making data useful… So, what other new KPIs should we be considering in addition to labor market mismatch?

Stefan: Another crucial KPI is obsolete skills.

Laura: I’m not entirely comfortable with how this sounds…

Stefan: Well we do need to accept that certain skills are becoming outdated or becoming less useful as technology and industry evolves.

Laura: Right…

Stefan: But tracking helps us stay ahead of the curve and ensure that both individuals and organizations can adapt to new demands.

Laura: For instance?

Stefan: For instance, if we identify that demand for a particular technology is declining, educational institutions can adjust their curricula accordingly to prevent students from investing time in skills that may not be as relevant in the near future.

Laura: Right… that’s concerning, especially for those whose skills might become outdated faster than they can adapt…

Stefan: Exactly and just to mention, we have to consider that not everyone has the time, capacity, or inclination to upskill which brings me to another KPI we should be focusing on…

Laura: Let me guess, is that related to location and travel?

Stefan: Correct, another valuable KPI is workplace flexibility. This measures how well jobs are adapting to evolving trends like remote work and gig work. By analyzing this, we can better understand how the workforce is shifting in terms of lifestyle preferences and organizational culture.

Laura: Like now, for example, we see a trend of wanting to work 100% from home or having a hybrid job.

Stefan: Yes, businesses need to adapt their HR practices to accommodate these shifts. It also reflects how well companies are supporting flexible work arrangements, which can impact job satisfaction and productivity.

Laura: That’s really eye-opening… Workplace flexibility is definitely a key trend in today’s work environment. Are there any other key indicators we should be aware of?

Stefan: Absolutely. You mention before low wages.

Laura: Yes…

Stefan: We also need to track income inequality within the labor market. Because the traditional metrics don’t capture the full picture of income distribution. Tracking income inequality helps us understand if wage gaps are widening between different demographic groups and whether workers are earning a living wage. For example, if we see that certain groups are consistently earning less than others despite similar job roles and qualifications, it indicates a need for policies to address these disparities.

Laura: Right, this is a very important KPI. We are seeing already that low-wage jobs are becoming harder to fill.

Stefan: Correct and this is a huge problem because we need these workers. Growth in employment has to translate into fair and equitable earnings for all and we are far from such a scenario…

Laura: So, in a nutshell, we’re broadening our view from just “who’s working” to “how well are they working, how adaptable is the workforce, and how fairly are people being compensated?”

Stefan: Precisely. In the long run, these new KPIs will give us a much more nuanced and accurate understanding of labor markets. It’s not enough to know that people are employed—we need to ensure they’re in sustainable, high-quality jobs that allow for growth and security.

Laura: What would you say is the key takeaway for our listeners—whether they’re policymakers, business leaders, or even job seekers themselves?

Stefan: The key takeaway is that labor markets are far more complex than a simple unemployment rate can convey. To build a more agile, resilient workforce, we need to rely on comprehensive benchmarks and metrics. It’s crucial to have experts who can accurately evaluate the system and the results, and it’s 9:39 important that we set these benchmarks at a high standard. Understanding the full picture of the labor market is critical for long-term success.

Laura: Great. Stefan, thank you so much for sharing your insights with us today. We hope you, dear listeners, walk away with a deeper understanding of these metrics to prepare for the future. For more expert guidance and cutting-edge solutions, don’t hesitate to reach out to Stefan or to JANZZ technology. We’re here to help you stay ahead in the ever-evolving landscape of labor markets.

Stefan: Thank you. Bye.

Laura: Understanding the changing labor landscape is the key(KI) to staying ahead.. Check out our podcast for more intriguing topics. One more time, thank you for listening, and goodbye!

Laura: Hi everyone, welcome to a new episode! Today, we are closing the second season and apart from giving you a quick overview of all that we have learned until now, I wanted to speak about the future of HR tech. So, I have with me today our high-level support person, Connie Casanova, the Executive Assistant & Special Projects of our company, and basically the eyes that see it all. Welcome to the studio.

Connie: Thank you, Laura. Hi, everybody!

Laura: As I was saying, today I would like to have a look at the JANZZ journey and to also speak about the future of HR technologies.

Connie: Sounds good.

Laura: In recent years, HR technology has advanced significantly. As a company specializing in human resources tools and services since 2008, how have we reached this point of development and innovation?

Connie: Well, that’s a long, long story. So to make a long story short: by staying up to date, adapting, and transforming.

Laura: Right, technology has transformed industries, and yet, we are still experiencing ongoing changes.

Connie: Definitely, there’s a lot more to come…

Laura: What does JANZZ.technology offer?

Connie: We offer a wide variety of products in the field of occupational data. For instance, in the hiring process, our intelligent job matching tool helps structure occupation-related data, providing analytics and reports to help align skill supply and demand in the labor market, conduct gap analysis, and offer career guidance. We also provide expert advice on data structuring, ontologies, and semantic matching, among other services.

Laura: Who can benefit from it?

Connie: Generally, we mainly assist government institutions and Public employment services. However, we have also engaged in B2B collaborations, extending our support to large corporations and organizations when needed.

Laura: What makes JANZZ.technology solutions outstanding?

Connie: We’ve remained consistent with our approach from the beginning. We knew that in order to build powerful HR tech tools, we needed to first create something that could help us understand and organize data. Machines can’t do it on their own. This is what almost all existing solutions on the market still lack…. We essentially needed something akin to a human brain, which is how our journey into building our ontology began.

Laura: For our new listeners, could you provide a brief explanation of what an ontology is?

Connie: Picture a vast library filled with encyclopedias covering occupation data worldwide. These encyclopedias explain all sorts of jobs, work with numerous job classification systems, and encompass both hard and soft skills related to various occupations, along with training and qualifications. Each of the concepts you can find is interconnected and available in more than 60 languages. So, it is a comprehensive multilingual system for occupation data.

Laura: How does it help?, and why is this JANZZ approach of using ontologies better?

Connie: First, because unlike other solutions or ontologies, it is meticulously structured and continuously updated to incorporate new work trends. Second, because we have been hand curating this data with experts since its creation, investing more than 900,000 hours since 2009 to build it.

Laura: That’s an incredible amount of work.

Connie: Absolutely, but that’s what enables us to create powerful tools like our matching engine or the parser. We knew we needed to take a different approach from existing solutions. Keyword matching systems, for example, can be quite frustrating, as we’ve all experienced…

Laura: Yes, don’t remind me of those awful times of unemployment. Getting this out of the blue job recommendations and having to search and scroll through lots of offers…

Connie: Exactly, or the tedious task of repeatedly writing the resume data because every company website has its own system.

Laura: Right, making this process more pleasant is definitely something I would have loved when I was looking for a job.

Connie: And it’s not just for job seekers; our tools also benefit recruiters, hiring teams, and government institutions working on the labor market.

Laura: I know and it’s amazing, but that’s just the matching tool we are talking about, right?

Yeonjoo: If we focus strictly on job searching or candidate matching, yes, but JANZZsme! functions the way it does thanks to the ontology and the parser, among other components. Our different solutions can work independently or in combination.

The JANZZparser extracts entities, the different classifiers organize the data as needed, JANZZ.jobs is the platform that brings them together, and we also offer the JANZZ.jobsAPI as a direct and uncomplicated interface. Depending on the specific challenges our clients face, we offer either standard or tailor-made solutions.

Laura: Can you give me an example?

Connie: For instance, in cross-national comparison, we noticed that in countries with a strong tradition of standardized training, much more emphasis is placed on vocational training than on work experience as a requirement in job postings… We also found varied emphasis on soft skills: in DACH countries, 90% of job postings for carpenters explicitly list soft skills. In the UK, it’s less than 50%.

Laura: Why is HR tech so crucial for modern businesses?

Connie: HR tech is indispensable in our fast-paced society, where efficiency and accuracy are paramount. These tools streamline HR processes, adding significant value to organizations. JANZZ.technology’s approach extends beyond anonymization. Our semantic matching engine assesses skills, experience, and context, revolutionizing job matching. Public Employment Services opt for JANZZ.jobs due to its scalability, rapid implementation, and cost-effectiveness, relieving them of managing IT departments.

Laura: Connie, can you share some insights into how HR tech benefits organizations and society as a whole?

Connie: HR tech streamlines tasks, boosts engagement, and aids data-based choices. It keeps advancing with smarter analytics, AI insights, and worker-focused tools, helping HR teams or public employment services build inclusive, productive environments.

Laura: What will be the future of the labor market when we think about new technologies?

Connie: The experts say that we will see even more integration of AI and machine learning, predictive analytics, and personalized employee experiences.

Laura: What about legal frameworks?

Connie: Good question! We can expect a growing emphasis on data protection regulations and the responsible use of AI in the future.

Laura: In this second season of the podcast, we tackled lots of interesting topics to help our listeners gain a deeper understanding of the current landscape. Shall we briefly recap some of the key points?

Connie: Of course!

Laura: We began by exploring the complexity of designing technology that effectively facilitates the matching process between resumes and job descriptions. This complexity arises from the fact that a candidate’s suitability for a job extends beyond their job title or previous roles. Numerous factors come into play, including skills, experience, knowledge, and context.

Connie: Exactly, and we can also match candidates with suitable job opportunities and training and education opportunities, helping people find the right training opportunity, whether it’s online or offline. This approach, similar to how we find the best available or matching job, can close skill gaps and improve the employability of jobseekers.

Laura: That’s right, and we also delved into the constantly evolving job market and how some professions necessitate specific authorizations or licenses. It’s not always the question of whether someone is capable of doing a job; sometimes, it’s also about whether the person is entitled to work as a nurse, a pilot, a lawyer, a forklift operator, social worker, teacher, and so…. There is no one-size-fits-all standard for skills profiles. Every role has its unique requirements.

Connie: So true. Throughout our episodes, we emphasized the intricacies of our approach and why transparency and ethics are paramount. While AI is a valuable tool, responsible technology adoption is crucial. The “I” in AI ultimately stems from the human team behind the programmers and the carefully selected training data.

Laura: We also provided a deep dive into the differences between explainable and interpretable AI and discussed how to ensure that AI technology remains ethical. In HR tech, selecting the right technology is essential as it directly impacts people’s lives, and any deviation from ethical practices could result in fines or even worse consequences.

Connie: Absolutely. It’s essential to remember that responsible technology adoption is not just a choice; it’s a necessity.

Laura: Thank you Connie for sharing your insights and experiences with us today.

Connie: My pleasure, Laura.

Laura: For those who haven’t already, I encourage you to listen to the other episodes of season two, where we explore the dynamic world of HR technology and its profound influence on recruitment and the future of work. As we close this season, we’re excited to announce that the upcoming season is just around the corner! We’ll be diving even deeper into the evolving landscape of HR tech, bringing you more insights about AI technologies, in-depth analyses, and more thought-provoking discussions. We will also maintain a critical eye on all the emerging snake oil surrounding new AI technologies.

We’ll discuss why it’s dangerous to bet on generative AI and why our AI approach is more ethical and risk-free. So, stay tuned and be prepared to explore the future of HR technology with us. Thank you for listening, and goodbye!

Laura: Hi, everyone. Welcome back to a new episode. I’m your host Laura Flamarich and today, we will be talking about the ethics of artificial intelligence both in general and specifically in our expertise field of HR tech. To delve into this topic, I have invited Yahel Appenzeller, one of our expert curators.

Yahel: Hello, everyone.

Laura: Thank you for joining us again!

Yahel: A pleasure.

Laura: Since AI technologies have become an integral part of our lives, ethical concerns have appeared regarding their potential impact on society, individuals, and the environment. So, if we start diving into the brother context of Artificial Intelligence, why is it so important to consider ethics?

Yahel: Well, Artificial intelligence has immense potential to bring positive change, but it also poses unique challenges. Without ethical guidelines, AI could reinforce biases, invade privacy, and deteriorate trust in technology itself. So, by incorporating ethics, we ensure that AI improves human experiences and well-being rather than restricting them.

Laura: Right, even artificial intelligence has been around and in use for years, it’s now, since this generative AI “boom” that people start to worry about the consequences. Let’s explain a bit what this is so that everyone can follow.

Yahel: “Generative AI” is a type of AI that’s able to create fresh things such as art, music, or writing by studying things that already exist. For instance, earlier this year in May, a person on TikTok used generative AI to produce a brand-new song that perfectly copied the voices, lyrics, and musical styles of Drake and The Weeknd.

Laura: Correct. The company that manages the artists’ music claimed that it was too similar to their work and shortly after, the video was taken down from TikTok and other places. But did the song really violate copyright? What other options do artists have to fight against AI-generated music? Can the song protect itself? AI-generated art doesn’t qualify for copyright protection but ‘materially affect’ the music industry.

Yahel: Exactly and we have seen a similar thing in the Hollywood industry this June, where actors and writers started striking because they consider the regulations are not specific enough in order to protect them.

Laura: This applies to many fields, and not only creative ones. The problem is not the generative AI itself, that was just an example, but the entire AI.

Yahel: Yes, think about the ethical implications in healthcare. One real concrete misuse was the case of the “Predictive Policing” algorithm used by Optum in 2019.

Laura: What happened?

Yahel: The developed algorithm intended to identify patients who might benefit from extra medical care to prevent hospitalizations but the algorithm ended up disproportionately selecting white patients over black patients for this extra care.

Laura: So the algorithm was biassed.

Yahel: Exactly, the algorithm was based on historical medical costs, influenced by systemic racial disparities in access to and treatment of health care.

Laura: This example to me is clear. Even when developed with the intention of improving patient care, AI algorithms can perpetuate existing biases and inequalities if not designed and tested carefully.

Yahel: Yes, it serves as a stark reminder that while AI has the potential to transform healthcare positively, ethical considerations are paramount to ensure that such technologies do not amplify disparities.

Laura: Right. This deteriorates public trust in AI technologies and can interfere with their adoption. So what steps can we take to ensure that AI ethics are integrated into technology development and deployment?

Yahel: It starts with a collaborative effort involving AI developers, policymakers, researchers, and the public. We need clear guidelines and regulations that address issues like bias, privacy, accountability, and transparency. Companies must prioritise ethical considerations in their development processes, and governments should create frameworks that hold them accountable.

Laura: Let’s move to our expertise area. HR technologies. Not everyone likes the idea of an AI system making decisions. Why is that the case?

Yahel: Well, this is probably because there is no agreement on a global strategy for defining common principles, which would include rules on transparency, privacy protection, fairness, and justice. And also in this field, there have been too many scandals involving gender or racial bias.

Laura: What should a company that uses AI in the hiring process do to avoid such a bad reputation?

Yahel: If businesses want to grow beyond scale healthily… They have to start with ethical recruiting. There’s no other way. If all AI were based on ethical principles, this would not happen, and people would see the positive implications…

Laura: Why is the use of AI in the hiring process positive?

Yahel:Well, AI is a win in many directions. Hiring has always been lengthy and arduous, and AI, for example, helps cut down on time spent recruiting great potential candidates.

Laura: AI can automate time-consuming recruiting and hiring tasks, yes, but how do we know we accurately identify suitable candidates or employers for a position?

Yahel: AI can ensure diverse and fair selections and reduce workplace bias—a goal organisations often strive to achieve.

Laura: But how do we ensure that we are not discriminating?

Yahel: Well, we need ethical recruiting. So AI ethics.

Laura: So, how can businesses implement ethical AI recruiting?

Yahel: Building ethical AI requires a solid ethical foundation throughout the organisation and possibly beyond.. As well as a clear understanding of how hard it is to develop and deploy genuinely transparent and fair AI systems.

Laura: What are the characteristics of a fair and transparent AI system?

Yahel: Here at JANZZ, we follow seven principles: The first is that technology should be designed to support humans, not replace them.

Laura: This means that saving us time doesn’t mean we need fewer people on the HR team.

Yahel: Exactly. In the end, a human specialist must make the final decision; technology or anything else shouldn’t execute it.

Laura: Ok.

Yahel: Then we have the personal data. We have to reduce bias, and AI is perfect for that!

Laura: So, how does that work?

Yahel: Our systems consistently separate personal and job-related information.

Laura: So the systems do not use the personal data for anything.

Yahel: Exactly, only job-related data is applied. Our system uses only anonymised, job-related data in the matching processes.

Laura: And how do we know that the AI criteria are ethical and unbiased?

Yahel: To ensure unbiased results in parsing, matching, classification and analytics, we use hand-selected and carefully calibrated gold-standard training data for our machine-learning models. So processes are fair, transparent and non-discriminatory.

Laura: What else is essential?

Yahel: It is essential to support organisations and adhere to best practices for fairness and inclusion.

Laura: How?

Yahel: By providing separated applicant/employee information into anonymised, job-related data. For instance, we anonymise personal data that includes crucial demographics such as gender, age, race, and ethnicity…

Laura: If we anonymise and hide some information, what happens to transparency?

Yahel: Our solutions are based on fully explainable AI and human-in-the-loop models. This means it is designed in a way that its decision-making process can be easily understood and explained. In other words, it’s not a “black box” AI where you can’t discern how it arrives at its conclusions. Instead, the inner workings of this AI are transparent and can be articulated and justified.

Laura: So explainable and interpretable AI.

Yahel: Exactly.

Laura: (To our listeners, if you haven’t heard the last episode of the podcast, was exactly about this topic). You also mentioned human-in-the-loop models…

Yahel: Yes, It means that the AI is not entirely autonomous; instead, it collaborates with human input or oversight. This can be important for various reasons, including quality control, ethical considerations, and handling complex situations that require human judgement. With this, we ensure all results and intermediate steps make rational sense and that rankings and analytics are not based on false, machine-generated relationships.

Laura: That sounds good, Yahel. So, with the collaboration between humans and AI, how do you make sure the AI models always produce reliable results and avoid generating false relationships?

Yahel: At JANZZ we are using human and machine intelligence to create machine learning models. We consistently foster accountability in our AI systems throughout their lifecycle by training them with hand-selected gold-standard data, monitoring every critical step and ensuring the systems function as intended.

Laura: Can the developers and users of the system also explain their decisions?

Yahel: Exactly, Laura, they can and it is clear who is responsible for which decisions and to whom. To this end, we proactively educate ourselves, our customers and our users about our solutions’ capabilities, opportunities and limitations.

Laura: Ok, so we have AI technology that benefits people, reduces bias, supports diversity, is fully explainable and establishes accountability.

Yahel: Yes, and then we have the privacy issue. Our technologies and solutions comply with all data privacy and security regulations of the respective markets and countries where we operate.

Laura: This is essential. Do we have it all?

Yahel: One more! Last but not least, fostering trust is essential.

Laura: Of course, how do we develop trust in AI technologies?

Yahel: At JANZZ, we take an active role with our customers and users through a multifaceted approach. We adhere to and advocate for our ethical principles, the OECD’s principles on AI, and the strictest regulations on data privacy and security.

Laura: Following strict regulations, you ensure compliance with all international and national guidelines and regulations, right?

Yahel: Yes, when it comes to trust, you have to be serious. We provide our customers with in-depth training and expert support in change management so that everyone knows the facts.

Laura: And with that, we have the seven core ethical principles for AI in HR technology. Which (in our case) applies not only to the AI lifecycle but to our entire organisation. Summarising, AI ethics can only emerge from a holistic ethical approach to organisations building and deploying AI technologies. Make sure your company recruiting system benefits people and that your AI serves the greater good of people.

Yahel: Well said, and if listeners want to learn more, they can find more information on our website, janzz.technology.

Laura: Exactly, or they can simply contact us. Thank you Yahel for joining and thank you for listening. Goodbye!

Yahel: Goodbye!

Laura: Hi, everyone; my name is Laura Flamarich and I am the video producer and digital media specialist of JANZZ.technology, and today we are here to speak about a topic that concerns two critical computer science concepts: explainable and interpretable AI. As artificial intelligence continues to shape various industries, it’s crucial to understand these concepts and their implications for responsible AI deployment. Joining us to delve into this topic is the Founder & CEO of JANZZ.technology, Stefan Winzenried. Welcome back to the studio!

Stefan: Hi everyone.

Laura: The distinction between explainable and interpretable AI isn’t always clear, leading to confusion. With AI techniques becoming more complex and opaque, it’s crucial to clarify their differences to ensure ethical and unbiased AI decision-making.

Stefan: A pleasure.

Laura: Let’s start this exciting conversation. One of the key steps in finding talent, especially in tight labor markets, is effective job posting. And one of the most important aspects of good job postings is determining the skills required for the job. This can be quite time consuming. So, with the rise of AI in HR tech, many recruiters and hiring managers want to know if these powerful technologies can help this process, say, by generating one-size-fits-all skill profiles for a given profession. Yeonjoo, is this possible?

Stefan: Exactly. As AI systems become more prevalent, understanding the mechanisms behind their decisions becomes paramount for identifying and addressing potential biases and errors. If we want to identify bias or correct any mistake, we must first understand how these techniques reach decisions.

Laura: Right. So let’s break them down. What is Explainable AI?

Stefan: By definition, Explainable AI models “summarize the reasons for [their] behavior or produce insights about the causes of their decisions”.

Laura: What does this mean?

Stefan:The goal in Explainable AI is to help us (humans) understand why the algorithm made the decision it did.

Laura: Ok. So Explainable AI explains the reason or the cause of that particular decision or recommendation.

Stefan: Yes, in a human-readable explanation.

Laura: So we know “why” they were reached but not “how” that happened.

Stefan: Yes, it’s hard to know how a result was reached, but you mostly know why, if you trust the explanation of course…

Laura: Ok, and Interpretable?

Stefan: Interpretable AI refers to AI systems that “describe the internals of a system in a way which is understandable to humans”.

Laura: So we understand the HOW.

Stefan: Yes, how an AI algorithm works, understanding the model architecture, the features used, and how those features are weighted…

Laura: Should we give an example of them to help understand what that means?

Stefan: Of course. Let’s consider a practical scenario using a matching engine as an example. Imagine we have a system that matches resumes of candidates with job offers. In this case, an interpretable system would provide insight into the “how” by allowing the designers to comprehend the way various factors such as job titles, skills, and experience, along with their respective weights, contribute to determining the match results.
Whereas, in contrast, an explainable system would offer a written explanation that sheds light on the decision-making process. For instance, it might reveal a candidate’s strengths and weaknesses, which is valuable in certain contexts. However, you don’t know exactly how this information played into the matching score and thus you cannot reproduce the result yourself.

Laura: That’s a comprehensive explanation! But Stefan… How can we know if the explanation provided by the system truly corresponds to how the AI algorithm made its decision?

Stefan: Depending on how the AI system is designed, there might be a gap between the explanation provided and the actual reasoning of the system. This can happen due to the complexity of some algorithms, where the internal processes are intricate and difficult to translate into a simple explanation. So, while the explanation gives us some insight, we need to be cautious about assuming it perfectly represents the AI’s decision-making path.

Laura: So… even with the written explanation, there’s still a possibility of missing out on important details that influenced the outcome?

Stefan: Precisely, Laura. It’s a nuanced challenge. The written explanation might not capture every factor that played a role in the decision. There could be hidden biases or other factors that the explanation doesn’t highlight. This brings us back to the importance of evaluating AI systems meticulously, considering potential biases and ensuring that the explanations provided are as accurate as possible.

Laura: Makes sense…. Is it better then, to use an interpretable system?

Stefan: It is definitely easier to identify and correct bias and build trust. Because as we mentioned, with them we know precisely how the results are generated. They are reproducible and consistent, as well as accurately explainable.

Laura: This sounds good…

Stefan: Yes it does, however, we still face the challenge of correctly processing the data… If your system can’t properly deal with the input data. The mistakes in the processing will lead to spurious reasoning.

Laura: Certainly… If we feed biased data, the outcome will be as well biased. We also discussed this in a previous podcast; poor data will consistently yield poor results. How can we effectively process the data and avoid this corruption?

Stefan: Your system must, at the very least, be able to accurately recognize and normalize the pertinent information within a dataset. For example, at JANZZ, we employ a range of tools to carry out this process. We utilize a parser that extracts and identifies all entities, specifically from documents like resumes, job offers, and related files such as cover letters, letters of reference, certificates, and more. We then integrate this parsed information with our ontology, that allows us to contextualize and provide perspective by presenting all associated data. This step is crucial in ensuring that the extracted data is put into the right context. In parallel, we proceed to normalize the data, refining it to maintain consistency and coherence in more than 60 languages.

Laura: For those not familiarized with our technologies or not having the technical knowledge of what this means. What do we need to understand exactly?

Stefan: No system is perfect, but for sure we need benchmarks and metrics. We require experts that are actually capable of evaluating the system and the results. High-quality data is crucial. Furthermore, we may still want to build a purely ML-based machine that can correctly process the data. After this, the next step is to process the data into an interpretable system and produce fair and accurate results with truthful explanations that can be understood by humans.

Laura: This is a valuable input, Stefan! Could we think of both Explainable and Interpretable AI?

Stefan: If we want the system to be used in any part of an HR tech stack, it must be both explainable and interpretable. Arguably more important is the fact that HR tech is used in decisions that strongly affect real people’s lives and as such, must be transparent, fair and in all respects ethical. And even if your system is only going to produce job recommendations for users, explainable results could be an excellent value proposition by building trust and providing users with actionable insights. And with any HR analytics tool, you should want to know how the tool arrived at its decision or recommendation before acting on it.

Laura: Of course. Sometimes we just assume and forget to properly check what kind of technology we are using. When I think about all these Terms and Conditions that we so easily accept. Imagine with AI technologies where the lack of knowledge the majority has is so little…

Stefan: Even worse… think about the Public Employment Services. I can give you a big list of countries in Europe that don’t comply with the current and even less with the upcoming and all the new regulations. They have no idea if they are compliant or not.

Laura: This has to change one way or another, and transparency appears to be crucial in this context…

Stefan: Absolutely, Laura. Transparency not only helps build trust in AI systems but also empowers us to identify and address any shortcomings. By striving for transparency, we enhance our ability to understand, scrutinize, and improve the decisions made by these systems. These issues could cause difficulties in a legal dispute, particularly because most people are unaware of the potential inaccuracy of these explanations.

Laura: Yes we have seen it in the US already, for instance with the complaint against Workday for using discrimination AI, and I constantly hear about the European Union on the news. Regulations are coming, and faster than everyone thinks – in all kinds of markets across the globe. Specially on explainability and interpretability in ML-based HR tech.

Stefan: Yes, as you’ve rightly pointed out, the landscape of AI policy is rapidly evolving, with numerous countries and regions actively engaged in crafting guidelines, regulations, and frameworks to promote Trustworthy AI. Organizations such as the European Union’s AI Act, AI Bill of Rights, Canada’s AIDA, and initiatives like the OECD’s AI Principles exemplify global endeavors to address these concerns. There’s even an initiative within the Association of Southeast Asian Nations (ASEAN).

Laura: It’s worldwide and should be a concern for everyone… So… as we anticipate the official implementation of more legislation, how can we proactively ensure the responsible use of AI technologies?

Stefan: As we said. Start by properly understanding what kind of AI system you are using. AI technologies have to benefit society and avoid having negative impacts or risks. Good AI systems must be developed and deployed in a responsible and ethical manner. They have to be accurate, reliable, robust, ensure safety, have non-discriminatory practices, they have to be secure against hacking or manipulation, transparent and accountable, explainable and interpretable, and provide data protection.

Laura: That’s a comprehensive list of criteria for responsible use!

Stefan: It’s a long list that unfortunately very few developers and users take seriously. And it should be a must when implementing these technologies. May we even think about generative AI?

Laura: EEEEH… This is going to be a tough one to regulate. Let’s concentrate on what we’re familiar with. As we wrap up, what advice would you give to organizations and individuals who are looking to adopt AI technologies while ensuring ethical and responsible use?

Stefan: My advice would be to start with a solid understanding of the principles we discussed: accuracy, safety, transparency, fairness, explainability, interpretability and more. It’s important to assess the potential risks and benefits of AI deployment in your specific domain and context. Engage experts and collaborate with diverse stakeholders to ensure a holistic and balanced approach. Contact us in the HR tech case! And always keep in mind that the goal is to create AI systems that augment human capabilities and well-being, rather than replace or harm them.

Laura: Thank you, Stefan, for shedding light on these important aspects of AI. It’s clear that as AI becomes more integrated into our lives, we have to pay significantly more attention toward responsible developments and implementations.

Stefan: My pleasure, Laura. It’s crucial that we approach AI with a commitment to ethics, transparency, and societal well-being. Only then can we truly harness the potential of these technologies for the betterment of humanity.

Laura: Well said.

Stefan: Shortly, I’d like to gently mention that at JANZZ ,for quite a long time we embodied AI Ethics principles, and it’s a good practice for all companies striving for fairness to make these principles public on their websites. If you are utilizing AI technologies and cannot locate any information pertaining to ethics or transparent explanations of the functionalities on the provider’s website, it’s worth asking yourself: “why?”

Laura: Good hint! Thank you to our listeners as well for joining us in this insightful discussion. Remember, as AI continues to shape our world, it’s up to all of us to ensure its responsible and ethical use. Contact us for more, check our website (janzz.technology). We have plenty of information which has an impact on you and we are here to help as well as to offer magnificent tools to treat data ethically! Thank you very much for listening and goodbye!

Stefan: Goodbye, everyone!

Laura: Hi, everyone. Today we will converse about one of our favorite topics, standard skill profiles, and for that, I have invited our Senior Ontology Modeler, Yeonjoo Oh

Yeonjoo: Hi, everybody

Laura: Thank you for being here with me one more time.

Yeonjoo: A pleasure.

Laura: Let’s start this exciting conversation. One of the key steps in finding talent, especially in tight labor markets, is effective job posting. And one of the most important aspects of good job postings is determining the skills required for the job. This can be quite time consuming. So, with the rise of AI in HR tech, many recruiters and hiring managers want to know if these powerful technologies can help this process, say, by generating one-size-fits-all skill profiles for a given profession. Yeonjoo, is this possible?

Yeonjoo: Well, there are certainly HR tech vendors out there who would want you to believe that. But I’m not so sure.

Laura: Why’s that?

Yeonjoo: Let’s take an example. I recently tested an online job optimizer that suggested waste management as a skill for a refuse collector. According to the vendor’s own definition, waste management goes way beyond what’s typically required for a frontline refuse collector. On the other hand, the vendor’s skill catalog includes the skill waste collection. Despite its obvious relevance to a job posting for refuse collectors, it wasn’t one of the suggested skills.

Laura: Why do you think that happened?

Yeonjoo: I guess there are two main issues. The first is that these systems are often error-prone and produce spurious suggestions. Oftentimes the AI relies on data that hasn’t been processed correctly and the mistakes made at ingestion, so when the data first enters the system, multiply as the data is moved along the pipeline. This has a significant impact on downstream processes and the results. For instance, there is a lot of variation in the vocabulary used to express skills.

Laura: Different people use different words to describe the same thing.

Yeonjoo: Exactly. And the system processing those words needs to translate them into some standard vocabulary. We call this normalization. So, in my refuse collector example, maybe the skills mentioned in job postings processed by the system were normalized to waste management instead of waste collection. By the way, this must’ve happened with the only other skill suggested in that example, too: community property, which is a legal framework to determine how ownership rights are held between spouses. I’m pretty sure this doesn’t have much to do with refuse collection.

Laura: Yes, we see this a lot. I remember seeing yoga as a top skill for freight handlers once. (Laughs)

Yeonjoo: Well, it’s a good way to relieve stress and wind down after a hard day’s work. Maybe it’s a new health and safety requirement. But, yes, it probably has more to do with faulty data processing and poorly performing semantic technologies. That’s the technologies that, in a sense, translate the many linguistic variations into a common vocabulary by focusing on the meaning of words, phrases or sentences. And then use that focus to process the data more accurately.

Laura: Yes, given the diversity in skills vocabulary, it makes sense that these technologies are super important when you’re dealing with skills. What about this new trend of using AI to write for us? ChatGPT, for instance, could that be used to standardize the vocabulary in resumes or job postings?

Yeonjoo: I’m 100% sure that a tremendous amount of resumes and job descriptions are generated by ChatGPT already. In fact, we have recently started noticing the impacts of text generators like ChatGPT on resumes and job descriptions that we have parsed and annotated. Some enthusiasts are sure that these types of AI services can advance matching by increasing the accuracy of skill sets, but I doubt so.

Laura: Why that?

Yeonjoo: What they define and provide as so-called “skills” is just too generic. This is the second issue with AI-based job posting optimization I hinted at earlier. Any AI-based system uses statistics to produce results. For job postings, the general logic is that popular skills are relevant to all open positions with a given job title. So the system is fed with lots of data, typically from existing online job postings. AI is then used to generate a list of skills most commonly associated with the job title you give it. AI-based text generators like ChatGPT may rely on job descriptions found on popular websites to produce a list of skills. But no matter the source, these AI-generated lists are generic. They typically include either too many or too few skills relevant to your specific open position. They also can’t prioritize the skills for you because focus and priorities shift depending on the individual position.

Laura: So you still need to go through all the suggestions and work out which ones are relevant for your job posting.

Yeonjoo: Right. And work out if there are other relevant skills that the system hasn’t suggested. When it comes to niche jobs or country and culture-specific needs, these systems can’t help either. There simply isn’t enough training data to produce meaningful statistics about commonly required skills. But more importantly, these systems are built on faulty logic.

Laura: How so?

Yeonjoo: They’re all built on the premise that standard skill profiles exist. So the pertinent question is do these standard skill profiles exist? Is there enough common ground in job postings across the globe for any given profession to define such a skill set? Or even in individual countries?

Laura: And?

Yeonjoo: JANZZ has analyzed millions of job postings over the years to investigate this. In fact we have a white paper on this topic. Maybe we can post a link in the show notes of this episode.

Laura: Sure, we can do that. So, tell me more. What did you find out?

Yeonjoo: So, to illustrate our findings, we explored skill profiles for carpenters and nurses based on several hundred job postings from the US, the UK, and the DACH countries: Germany, Austria and Switzerland. Our evaluation showed a vast amount of variation in skills required for individual positions, even in strongly regulated professions such as a nurse.

Laura: Can you give me an example?

Yeonjoo: For instance, in cross-national comparison, we noticed that in countries with a strong tradition of standardized training, much more emphasis is placed on vocational training than on work experience as a requirement in job postings…
We also found varied emphasis on soft skills: in DACH countries, 90% of job postings for carpenters explicitly list soft skills. In the UK, it’s less than 50%.

Laura: And what kind of soft skills are required?

Yeonjoo: That’s interesting too. In the US, the top 3 demanded soft skills were: physical fitness, flexibility and overtime, and teamwork (in this order). By contrast, the top 3 soft skills in DACH countries were teamwork, working without supervision, and reliability.

Laura: Ok, it sounds like employers in the US typically look for quite different workers than those in the DACH region. What happens if we still want to create a standard skill profile, even with all these differences? Is that possible?

Yeonjoo: The most straightforward strategy would be to include any criteria found in at least one job posting. Using our data for carpenters this resulted in a list of 103 requirements, most of which would be utterly irrelevant to an individual opening. And having to sift through 103 suggestions is simply a waste of time.

Laura: And if we search for a common denominator?

Yeonjoo: Sure. We could, for instance, take all skills required by at least 25% of job postings in each country. Again looking at our study, this leaves us with just two criteria for carpenters: work experience (of unspecified length) and a driving license.

Laura: Ok, that’s completely generic.

Yeonjoo:Exactly. And we’re talking about a skilled trade. In Switzerland, for instance, a completed apprenticeship is a must in most job postings for carpenters. In the US, 70% of job postings do not require a driving license. So this profile would result in many unsuitable candidates in one country, and many missed opportunities in another.

Laura: What happens if we focus on just one country?

Yeonjoo: Well, the list of skills is longer, but still too generic. Especially when you look at the specialist skills. There are so many specialties in nursing and carpentry, if you just look at the most commonly required skills, you don’t get any relevant suggestions for specialties with less online job data.

Laura: I see. And of course, this is true for many other professions, too. Even if you break it down further geographically.

Yeonjoo: That’s right, Laura; we can observe substantial variations in skill profiles within any geography simply because different businesses position themselves differently in the market. Differing company philosophies, team dynamics, and client expectations all manifest in different skill requirements for positions with the same job title.

There are so many factors that influence the skills needed for a particular position: There are educational, regulatory and cultural factors on a national or regional level. The industry plays a role, the hiring company. Just think of the different skills an accountant would need working for a large bank compared to an accountant for your local cafe. And of course the team. Maybe some skills are already covered by other team members, or you need a particular set of soft skills to blend in.

This is precisely what we’ve seen in our data. It means that any skill profile has to be adapted to the country, to the industry, to the individual business, and finally, to the particular position. Which brings us back to individual job postings. So, there really is no such thing as a standard skill profile.

Laura: So, back to square one.

Yeonjoo: Yup. Bottom line: Using mediocre automation both when writing the job posting and in the matching process more often than not ends up wasting time (and money) rather than saving it. Because you have to sift through unsuitable suggestions in both stages: you sift through unsuitable skills for the job posting, and unsuitable candidates for the position. So it’s very much worthwhile spending a little more time on carefully crafting an effective job posting for your individual open position by hand, and then using high-performing matching technology that surfaces truly suitable candidates.

Laura: Sounds like a solid plan. Thank you, Yeonjoo, for joining us today.

Yeonjoo: A pleasure.

Laura: To our listeners, we could carry on with this topic for hours. For instance, there is an entire discussion to be had on what constitutes a skill, how to deal with different levels of skills, translation and localization, and many more dimensions. If you are interested in digging deeper into topics connected to the challenges of skills and skills data, semantic technologies, matching, and AI, check out the other episodes of this podcast. If you want to read more about the study on skill profiles, I’ll leave the link to the white paper in the show notes. If there’s anything else you’d like to know more about, check out our website janzz.technology and feel free to get in touch. Thank you for listening, and goodbye.

Laura: Hi everyone. In today’s episode, machines versus humans! Why do you think we invest so much into “human” power at JANZZ to curate all our data? Well, let me introduce you first to my guest today. She is Theodora Sykara Lekaki, one of our ontology team members. Welcome to the studio!

Theodora: Hi Laura, thank you for the invitation!

Laura: Thank you for joining!

Theodora, let’s dive right in. Intelligence is typically defined as “the capacity for learning, reasoning, and understanding”. What would you say distinguishes human intelligence from Artificial Intelligence?

Theodora: Well, artificial intelligence is not even remotely comparable to human intelligence…. but to give you an answer, I’d say that Artificial Intelligence lacks one of the key markers of intelligence: the capacity for understanding.

Laura: I agree, an AI system is basically a sophisticated “bag of tricks” designed to make us believe that it has some understanding of the task at hand.

Theodora: Exactly. You see, intelligence in the human sense requires a key cognitive capability: storing and using commonsense knowledge, which humans develop through a combination of learning and experience.

AI systems simply don’t have this fundamental requirement. And won’t achieve it in the foreseeable future. Especially not the currently widespread AI systems. They rely exclusively on statistical or machine-learning methods, which are, by the way, also called knowledge-lean systems. Statistics just don’t generate understanding.

Laura: Yeah, a knowledge-lean system doesn’t sound like it’s going to leverage commonsense knowledge anytime soon… How do we develop AI-based technologies here at JANZZ?

Theodora: We take a knowledge-based approach, which means that we feed these technologies with extensive human knowledge. And it’s precisely this human-generated knowledge representation that ensures we can develop technologies that actually deserve the name artificial intelligence.

Laura: Today’s topic is very broad so let’s focus on our own expertise; the world of semantic technologies related to human resources and recruitment… Why is your job at JANZZ so important?

Theodora: Well, especially in the field of HR or job searching, the reliability of data and evaluations is crucial which is why counting on a human team is necessary.

Laura: Could you give me an example?

Theodora: Sure, the typical example of how real people can be affected by underperforming AI technology happens all too often:

For example when we have perfectly suitable candidates for a job position and they are discarded by AI-based systems like ATS just because their resume doesn’t contain the exact keywords specified in the filter or is associated with false contexts. Something that would most likely never happen if a conscientious human recruiter had seen the CV.

Laura: Of course machines lack commonsense knowledge.

Theodora: Exactly and commonsense knowledge is absolutely essential when it comes to understanding natural language. We humans acquire this linguistic competence at an early age and can use it to discern the meaning of arbitrary linguistic expressions. Knowledge-lean AI models will never be able to do so, to the same extent because they work on a purely quantitative basis: their ‘intelligence’ is based on statistical approximations and memorization of text-based data.

Laura: So what do AI machines understand?

Theodora: AI, or more specifically, ML (machine learning) systems can, at times, sidestep the problem of understanding and give the impression that they are behaving intelligently. But they will never actually understand the meaning of words; they simply lack the connection between form, namely, language, and content: the relation to the real world.

Laura: So machines don’t understand the words they are processing.

Theodora: No, they don’t. Think about it; commonsense knowledge comprises an inconceivable number of facts about how the world works.

We, humans, have internalized these facts through lived experience and can use them in expressing and understanding language.

Laura: Yes we humans, simply don’t have to encode this staggering amount of knowledge. We effortlessly understand the world.

Theodora: Yes and precisely because this tacit knowledge is not captured systematically, knowledge-lean AI systems have no access to it.

Laura: So the connections are based purely on cooccurrence, on what shows up together often. And because these systems do not store the meaning or meanings of a word, they often have great difficulty in discerning the nuances of everyday language.

Theodora: Yes and there’s more Laura. Machines make connections that we just don’t understand! Which makes the results difficult, if not impossible, to explain. And explainability is really important when you’re dealing with HR tech because of its direct impact on real people’s lives.

Laura: Absolutely. So tell me how we use humans at JANZZ to make our AI products smarter and reliable?

Theodora: First of all, our machine learning is supervised by people. Let’s take our job and CV parsing tool as an example. The JANZZparser! relies on natural language processing, a branch of machine learning. but always combined with human input: Our data analysts and the team I am part of (the ontology curator team) carefully and continuously train and adapt the language-specific deep learning models, supervising at every relevant step. And we are very careful with the training data.

Laura: Aha… I understand that many automated tasks in big data require a significant amount of human labor and intelligence… So tell me more…

Theodora: NLP tasks are trained using our in-house corpus of gold standard training data which we hand-select. Gold standard means that this collection must be the most accurate, reliable and unbiased dataset you can get. So a fair amount of our work involves collecting, combining, and annotating work-related data. And making sure that the corpus as a whole represents the highly diverse world of jobs, skills and other related concepts as accurately as possible, in particular also covering areas where online data is rarer.

Laura: And of course, this is very important because to train language models we require VAST amounts of texts, mainly from the internet. And if we would just feed this data to the machine without human filtering, the bias in the data – and therefore in the algorithms – would be huge!

Theodora: Yes, this is why even the most advanced language model is still biased. even though teams of experts have gone to great lengths. to try and tackle this problem through tuning the algorithms.

Curating these training data requires strong reasoning skills and real-world understanding, as well as problem-solving and creative thinking. Reasoning is already a huge challenge for machines and real-world understanding… Well, that’s just impossible for a machine without substantial human input. So there is no way around it: If you want reliable, explainable, unbiased AI systems, you need people.

Laura: Makes sense. So we use people to supervise our machine learning models and to collect and annotate the right training data. What else do we use people for in our AI technologies?

Theodora: Well, keeping with our example of the JANZZparser!, another very important step is that the parsed information is normalized and contextualized, which is key for further processing like matching or analytics. To do this reliably, the parser really needs to understand the content in some sense. This is where our hand-curated ontology JANZZon! comes in.

Laura: Yes, we have talked about our ontology before on this podcast, but let me remind our listeners that JANZZon! is the most comprehensive multilingual knowledge representation for job-related data that exists worldwide.

Theodora: Yes, this knowledge base that can be read by machines (so machine-readable) contains millions of concepts such as occupations, skills, specializations, educations and experiences that we humans manually link according to their relations with each other.

This is basically how we convey the context of a given concept to our machine learning models. The model can access the ontology to look up a concept and can see how it is linked to other concepts, how strong those links are, what type of relation two concepts share, and so on. So it’s a machine-readable representation of our knowledge of all these work-related concepts.

Laura: Theodora, you work mainly on Greek ontology. Could you give some practical examples of why these links/connections/relations are needed?

Theodora: Yes! For example, in Greek, we use the same phrase to describe a middle school math teacher and a professor of mathematics. So, if you were searching for a math professor in Greek using only keywords, you would come up with various irrelevant results.

At JANZZ, we teach our tools to understand the context of a phrase and then make informed decisions that significantly improve the accuracy of our search.

This is why we need the linguistic expertise and the cultural understanding of our people. Humans can read between the lines, and understand nuances and cultural differences. By adding all these aspects to our knowledge representation, we achieve accurate matching.

Laura: Interesting! Could you tell me more?

Theodora: Inside the ontology, the concepts are logically organized under main branches or categories, such as occupations, skills, industries and several others. These main branches are central to the matching process. Now, each concept has several codes and labels assigned to it, that further optimize the matching accuracy.

Laura: OK, so, like occupations, skills, experiences, and this kind.

Theodora: Yes, and each concept is annotated with various attributes like translations in different languages or classification codes.

Laura: Yes, we have over 100 official occupation classification systems, such as ISCO-08, the International Standard Classification of Occupations or ESCO, which is the multilingual classification of European Skills, Competences, Qualifications and Occupations.

Theodora: Yes, and we also have available over 60 other standardized reference systems as well as a variety of occupation classes.

Laura: What are these exactly? The occupation classes…

Theodora: That’s a type of classification we’ve introduced to quantify how specific a given job title is. Some job titles are very specific like Android App Developer, right?

Laura: Right, the job title says it all.

Theodora: Exactly, we don’t really need much additional information to match such a job to a person. But a title like a Consultant or a Manager. carries very little matchable information. It’s just too vague, or unspecific.

Laura: I imagine we could find such roles in practically ALL industries…

Theodora: Yes, and for a precise matching or classification, we would need additional information like, as you just mentioned, the industry or others like specialization, skills experiences, etcetera.

This is why at JANZZ we created these occupation classes. We use them to influence the weighting of the relations between concepts in the knowledge graph and in the matching process.

So, when comparing a job posting and a resume: the less specific the job title, the more weight other factors are given by the matching algorithm.

Laura: Ok, now I understand how we manage to get more accurate results for any kind of job title or role…BUT, how are the concepts in the knowledge representation related?

Theodora: Every concept is part of a vast map and all these concepts are interconnected in a meaningful way that reflects common understanding and job market practices.

Going back to the math professor example, a professor of mathematics is linked to very different skills, diplomas, experience and even soft skills from a middle school math teacher.

Every connection in our ontology is a conscious decision taken after careful consideration and is based on our understanding of language, culture and, most importantly, the real world.

Again, because our ontology is a representation of human knowledge.

Laura: So if I understand it correctly, we need to ensure that it contains the connections we humans make, our common understanding and contextualization of the world, as opposed to the unpredictable connections a machine might make.

Theodora: Exactly, as you can imagine, there is a great level of analytical thinking involved, and we need to consider a variety of factors before making even the smallest alterations. That’s why humans are important!

Laura: Yes, that is why we need curator experts like you Theodora. At JANZZ we count on the knowledge of 10 curators for every software engineer or data scientist we have. and thanks to the diversity in the team, we can cover more than 40 languages and almost 100 different industries!

Theodora: Yes, everyone in the team brings a lot of experience and knowledge from different fields and on top, we also cover different cultures and languages.

Laura: Cross-lingual and cross-cultural differences are one of our key success factors. If you want to learn more about our human team of experts visit our website or contact us. We will for sure come back with more episodes soon. And if you’d like to read more about knowledge-lean and knowledge-based AI, check out our article on the topic. I’ll leave the link on the description of this episode. Thank you Theodora for bringing your intelligence to the podcast today!

Theodora: A pleasure Laura!

Laura: Stay tuned and goodbye!

Laura: Hi everyone. Welcome back! Today we continue with part 2 of our Matching episode and this is why next to me I have our Ontology Curator Yahel Appenzeller!

Yahel: Hi everyone!

Laura: Thank you for joining us again!

In the last episode, we were saying that when it comes to skills matching, many, many details need to be considered. One category of information we didn’t mention last time is that related to personal characteristics, i.e. information like name, age, nationality, gender, religion, civil status, appearance, etc … and I want to point out that we don’t use that kind of data at all. When it comes to matching, what matters are the skills and other purely job-related characteristics we were talking about in the last episode. Personal characteristics are at best irrelevant and at worst insert bias into the matching process.

Yahel: True Laura. Fighting discrimination in the hiring process is a very important matter that we haven’t mentioned yet. And it’s not just the potential candidates that benefit… Nowadays businesses and the recruiting industry are placing increased emphasis on company branding to attract talent. At the same time, inclusive hiring strategies have become very important to the public. So, deploying bias free matching in the hiring process puts hiring companies in a very favorable light.

Laura: Good point!

Yahel: And when using AI technology, unbiased, explainable matching is not just nice to have, it’s going to be mandatory in Europe very soon with the upcoming AI Act. Which means it’ll also be mandatory for any company that operates and recruits globally.

Laura: Yes, we had a post on this topic recently, discussing the EU white paper “On Artificial Intelligence – A European approach to excellence and trust”. It’s about using AI in Human Resources… I’ll put the link to that post on our website janzz dot technology in the description of this episode. We’ll be dedicating a podcast episode to this topic soon, too…

Yahel: I’ll stay tuned then!

Laura: Excellent! And picking up where we left off last time… We were saying that we need to take care not to compare apples with oranges. Right Yahel?

Yahel: Exactly, let’s look at an example. Say we have a manager who is open-minded, communicative, strong in leadership and good at solving problems.
Without a meaningful context for this person’s role as a manager, we don’t know which industry the potential candidate is in. Retail, construction, finance, clothing… it could be anything. Without more specific information, the candidate would probably match to vacancies in any one of these industries, despite each such job most likely requiring specific industry insights or knowledge. There is no relevant experience to put the skills into a meaningful context.

Laura: So it’s not enough to just consider skills, we also have to take experience into account.

Yahel: Yes, experience is another part of the puzzle…and, by the way, the most complicated entity in terms of “matchability”. However, adding experience to the mix is still not enough to accurately match people to jobs and applicants to positions. As I mentioned in the last episode, there may also be other, let’s say, formal requirements like education or licences, which clearly need to be taken into account as well. But we need more.

Laura: What else is needed?

Yahel: Personality! Aspects like skills, experience and formal requirements don’t determine whether the new copywriter will fit into the team well, whether the new nurse will arrive at the hospital on time, or the new PR executive will perform well under pressure. A match only becomes truly successful if the applicant’s personality is considered too. My resume details a wide range of things I have done, but how I have done them is just as relevant.

Laura: So if we take job title, skills, experience and personality of a candidate into account, do we get the perfect match?

Yahel: It depends on what you match this against. After all, when you have a perfect match, the skills and personality of a new employee will complement the skills and personalities of co-workers. If I’m the only software engineer in a company, I need to be an all-rounder and take initiative with ease. If I am hired in a team with two others – one of them more familiar with field X, the other with field Y, both introverts – I’ll need to bring skills that complement and enhance those of the team as a whole. That way, our collaboration can create something completely new.

Laura: Oh, that’s a big challenge, how is it possible to know the team (if there is one) or the environment where a candidate will be working? It’s just not possible, right?

Yahel: Some clues may be found in the information contained in a job posting, but in general, this data isn’t readily available. That’s why matching software usually focuses on matching candidates and jobs without taking the team, the environment, etc. into consideration at all. This means you might get the perfect match for the role, but a very poor match for the team or company.

Laura: So can this be fixed? If you’re expected to fit in well with a team, then the prospective co-workers should also influence the perfect match.

Yahel: That’s right, Laura. Ideally, the co-workers’ profiles should be matched as well. And although this is perfectly feasible, there are surprisingly few matching solutions out there that actually incorporate this. But it’s definitely an interesting approach. With explainable matching software, you could even reverse-engineer this. You can create a profile for the team, based on their tasks and responsibilities as a collective, then use the matching for a gap analysis. Once you see the missing skills, experience and other aspects, you can determine what qualities you should be looking for in a new team member.

Laura: That sounds awesome! So what’s the catch?

Yahel: Well, in a nutshell: Because the problem is so complex, there’s simply no one-size-fits-all algorithm. There’s a big difference in matching a nurse or a kindergarten teacher, a marketing manager or a picker packer. So we need very different matchings based on the different activities, roles and occupations, surrounding teams and company environment. This is why we use multiple approaches at JANZZ. And then there’s the individual expectations for the outcome. Matching is driven by expectations and expectations change constantly. So we can only evaluate all the factors as far as possible in order to best approximate the perfect match. But one thing is sure: the only way matching isolated data fragments through arbitrary keywords will produce the perfect match is by pure coincidence. It’s like picking a candidate out of a hat. Except that would at least be cheaper…

Laura: I think I’d prefer a good approximation…

Yahel: Then you should go with a matching engine that considers all these factors we’ve been talking about – in an appropriate way: skills, experience, personality and former job titles. If these criteria are properly interpreted and weighted appropriately, you’ll get the best possible starting point to bring people and jobs together using technology.

Laura: You say ‘weighted appropriately’. What do you mean by that?

Yahel: How much emphasis is put on the various criteria in the matching process. For instance, the more senior the position, the more important experience becomes. If we think about a career starter, then education or other criteria play a more important role. Hiring managers and recruiters generate varying sets of must-have and nice-to-have criteria depending on the specific position. And job seekers have their own set of preferences and requirements too.

Laura: So perspective matters in matching too?

Yahel: Yes. It‘s quite possible that a candidate thinks they‘re a good enough match for a position and are happy to apply for it. Perhaps partly because the workplace is close to their home and they like the brand.
It can look quite different from the perspective of the potential employer. They‘re interested in quality over quantity and want candidates that meet their requirements as closely as possible – which often don‘t include criteria like how long the candidate‘s commute is. When we perform comparative matching tests for POCs or benchmarking, clients often look at conversion metrics like did this candidate actually apply for this position? But even a candidate that, looking at the job-related data we discussed, could be a perfect match from the employer’s perspective may not apply because of personal preferences, company branding, the later steps of the recruiting process like automated interviews or in-depth assessments etc. So this is more a question of behavioral analysis and in a sense, market research on the entire recruiting process. Matching as we view it is just one part of this.

Laura: Do you think, possibly by extending the scope of the matching, that this technology will replace recruiters anytime soon?

Yahel: I imagine it’ll be another few decades before these kinds of algorithms can identify the perfect hire on their own and truly replace the human element in the recruiting process. But I’m not sure if that should even be the goal. Sure, you can reduce bias significantly if you go about it the right way, and machines tend to be faster and cheaper than humans. But do we really want machines to make hiring decisions for us? Decisions that affect people’s lives to this extent? As far as we’re concerned here at JANZZ, our technology isn’t about getting rid of humans. It’s about getting rid of mistakes. We simply want to enable more efficient, unbiased processes that help recruiters and hiring managers to make better decisions and people get the jobs that fit them best… no more and no less…

Laura: Thank you so much Yahel. This ethical aspect is another super interesting topic in itself. We’ll be talking about that in an upcoming episode this season.

Yahel: You’re welcome!

Laura: Thank you for listening and be sure to tune in for the next episode of our podcast. Goodbye!

Laura: Hi everyone and welcome to the second season of this podcast! Today’s episode is about matching! And to speak about it I have with me Yahel Appenzeller from our Ontology Curator team!

Yahel: Hi everyone!

Laura: Matching is one of our core competencies, and no, we don’t make dating apps like Tinder or Bumble. But I bet if we did, the chances of finding the love of your life would be way higher than with the current apps…

Yahel: Haha. Well, I’m not sure how exactly matching works in dating apps but I’d imagine that the challenge of finding the right fit for a vacancy or a job candidate is a little more clear cut than finding a true life partner… What I do know is that, unlike dating apps or many job search sites, JANZZ matching never generates complete mismatches.

Laura: See, this is why you came today to the podcast, to speak about that. Wouldn’t the world be better if we could set some filters and parameters in an application and swipe for jobs or candidates that fit?

Yahel: It would definitely save time and effort for recruiters and employment services.

Laura: When I think about it…. finding a job is already a job in itself. And it’s an ordeal we all go through at some point in life – or even several times…

Yahel: Yes and not just for people looking for jobs. Recruiters, headhunters, hiring managers and employment agencies spend hours going through resumes and applications too. And with the current tight labor markets across the globe, finding and retaining talent is key – but to retain talent, you need to make sure the candidates are a great match from the start. And when it comes to finding candidates – recruiters are typically faced with either far too many applicants, or far to few…

Laura: Right, and nowadays we all have access to a vast amount of information… you can search for jobs and candidates all over the world!

Yahel: Yes, the market is now global and thus much larger – and much more visible. As a result, attractive positions get a lot of applicants and attractive candidates get a lot of offers. The question is, how many of those applicants or offers really are a good fit? Regardless of whether you use a national or global site, be it a job site or a professional networking site, or any other source, the results are in many cases inadequate, if not downright irrelevant. A lot of this has to do with the fact that the majority of search algorithms are based on fairly crude keyword-based matching, which typically not only produces a slew of irrelevant results, but also misses many good results because of the myriad vocabularies people use. There is also the challenge of varying interpretations of the different terms, and the many variations in how different aspects are weighted. What’s important in a candidate for me might be very different for you – even if we’re looking to fill the same position. The same goes for different people looking for the same type of job. There are also plenty of hidden expectations, like criteria that aren’t mentioned explicitly. All these aspects combined make matching a highly complex task and the right match very much a moving target.

Laura: Makes sense. And as you said, matching good candidates is very important. So how can applicants, recruiters or companies get a suitable match?

Yahel: Well, first let’s clarify that matching is the act of pairing entities that suit each other in some predefined sense, in our case a job and person.

Laura: Ok.

Yahel: Even in this context, the word ‘matching’ can have various meanings. In some jobs, whether a candidate is suitable for a given job can be fairly simple. If you are physically healthy, for example, have some stamina and can show up on time, you should be able to pick strawberries. There are other jobs, however, that require a variety of certificates, specializations and experience. Try to match candidates to a position as neonatal surgeon in a hospital and this becomes clear. For some positions, experience is more important, for others the training, the skills, the soft skills or the values… And the importance of the various criteria can also change depending on the stakeholders. So the process gets tricky pretty quickly.

Laura: True, there are a lot of details to be considered during the matching process…

Yahel: And the prevailing conditions are constantly changing. Requirements that were commonplace yesterday no longer apply today, and in turn, today’s requirements will no longer be valid tomorrow. The same is true for the jobs themselves.

Laura: Of course, who would have said that you could be a content creator for TikTok now? And who would have cited such specialization in his or her CV?

Yahel: How we define jobs, prospective employees and the labor market shifts all the time and this is a huge challenge when a machine has to deal with the task. Because there are very few constants and little to no universal “truths”.

Laura: Can a machine or an algorithm satisfy our matching expectations?

Yahel: Machines have to apply all the experience and knowledge of the recruiting specialist, in much the same way, paying attention to the smallest details, filtering the relevant information and adapting to changes in the labor market. As I said at the beginning, talent and businesses also use quite different vocabularies and descriptions, place emphasis on different aspects, have diverging interpretations of skills and levels of skills, and so on. And the datasets we want to match, say a candidate profile and a job description, are often very asymmetric. Meaning that one tends to carry a lot more information or criteria than the other. So there are multiple dimensions of complexity that come into play and need to be resolved by the software, or the underlying machine or algorithm. note

Laura: Right.

Yahel: Suppliers of this kind of software focus on different dimensions of the data to resolve this highly complex task. For example, former job titles of applicants or the associated skills are taken into account. An algorithm then compares job postings and resumes, and a match is made. Successful?

Laura: A match based on former job titles or skills? I don’t know – you tell me…

Yahel: Imagine, if a candidate had position X at company A, they can also hold position X at company B, right?

Laura: Yes?

Yahel: This may have held true in the past, to some extent. For a long time, jobs and job requirements were fairly clear cut, with more or less standard job titles like bricklayer, medical secretary, patent lawyer, janitor, etc. Today we have happiness heroes instead of customer service operators, digital overlords as website managers, and accounting ninjas as financial managers. So your algorithm needs to know what all these new, at times pretty obscure job titles correspond to. And there are challenges even with more traditional job titles: Is a sales consultant someone who works in retail and advises customers? Or someone who prepares offers, takes up orders and negotiates contracts with customers? Do they sell carpets or sensors for digital twin systems in manufacturing? Many job titles are either too generic, too specific or too obscure. And the titles companies come up with often describe functions as opposed to occupations. Without a more detailed description of the position and its context, we wouldn’t know whether an applicant is really suitable for a position, or vice versa – so how would a machine know?

Laura: Well, context definitely makes a big difference, I can see that. And much has changed in individual jobs, too, over time.

Yahel: Exactly. So, a good match also depends on how up to date or relevant a candidate’s experience is. After a certain number of years, your experience in some jobs might not be relevant anymore, or not as relevant, because the skillset has changed, the technology, the research, or the market. Whereas for other jobs this might not be an issue. If you were a programmer in the 80s and then switched careers, that experience will certainly not be relevant now. But if you were an experienced legal secretary in the mid 2000s, you will most likely still find a good match in a similar role. But of course, more recent experiences should carry more weight..

Laura: How can machines then make these distinctions efficiently?

Yahel: Well… Some job matching providers solve the matching problem by using other parameters – they look at skills and competencies since these represent the ‘content’ behind job titles. So, the matching algorithm considers the candidate’s skills and the skills required for a job and matches them. Skills-based or competency-based matching is more meaningful and thus promises better results because it takes into account not only a title previously held by an applicant but also that person’s knowledge, talents, insights and education. And many skills are transferable from one role to another, which can widen the talent pool in a tight market.

Laura: So the key is the skills.

Yahel: They certainly play a very important role. And apart from widening the talent pool for recruiters, skills-based matching also opens up opportunities for new career paths. For instance, a project manager could transition to a leadership role like general manager, or become an account manager, a business developer, or a consultant. With a few additional skills, an engineer could switch to technical project management, and a good lawyer could be a great lobbyist.

Laura: I see your point. Skills like critical thinking, management and leadership, analytical skills and creativity are transferable to other management and leadership positions.
So, can we say that skills are a reliable factor for machines to evaluate the perfect match for a vacancy?

Yahel: Well, it depends. When talking about skills, we need to make sure that we’re not comparing apples with oranges. The sales skills of a retail store assistant are very different to those of sales executive in med tech industry. This issue certainly has to be addressed before you can get meaningful results from a skills-based matching engine.
As I said, skills certainly play a very important role. But so does knowledge, education, and experience. And then there are jobs that require authorizations or licenses, like medical professionals or lawyers. All these various elements need to be taken into account – in the right context and with the right weighting. Something a good recruiter or hiring manager will do – consciously or not. So now all these different aspects need to be explained to a machine. And we haven’t even started on what it takes to match a candidate to a team…

Laura: Ok, I’d love to know more and I bet I’m not the only one. Let’s dive into this in the next episode.

Yahel: Let’s do that!

Laura: Thank you very much for coming.

Yahel: A pleasure!

Laura: And to our listeners, thank you too. Be sure to join us for our next conversation with Yahel.