Knowledge, skills and experience are three crucial components that make up an individual’s competence in any field. Unfortunately, these terms are used interchangeably these days, but they have very different meanings.

Knowledge refers to an intellectual understanding of facts, concepts and theories related to a particular field. It is acquired through education, reading books, attending lectures and participating in training programmes. Knowledge is essential because it provides the basis for developing skills. It enables individuals to understand the why behind a particular practice or procedure. Skills, on the other hand, refer to the ability to perform a task with consistent accuracy and quality. It is the application of knowledge in a practical setting. Skills are developed through practice, repetition and feedback from experienced mentors or supervisors. The more an individual practices a skill, the better they usually become at it. Experience refers to an individual’s exposure to a particular field or area of work. It comes from work experience, internships, volunteering and other practical applications of knowledge and skills. Experience is valuable because it gives individuals a real-world understanding of the challenges they may face. It helps them identify potential solutions to problems and provides opportunities for personal growth and development.

Applied skills, on the other hand, refer to the practical use of skills in a specific job or field. They are skills that an individual has developed through practice and experience and that can be readily applied in real-life situations. Applied skills are essential because they are the only ones that enable individuals to perform their jobs efficiently and effectively. While both skills and experience are essential, experience is always better than skills alone. This is because only experience allows individuals to apply their knowledge and skills in a practical setting. It enables individuals to develop problem-solving, communication and other critical skills that are difficult to learn through reading or training alone. Experience also provides individuals with a deeper understanding of the complexities of a particular field. It makes them more adaptable to change and more likely to succeed in challenging situations. In addition, experience provides individuals with the opportunity to learn from their mistakes and develop resilience.

In summary, knowledge, skills and experience are all essential components that shape an individual’s competence in any field. While skills and knowledge are valuable, experience is always better than skills alone. The practical application of skills and knowledge gained through experience provides individuals with a deeper understanding of their field, problem-solving skills and the ability to adapt to new challenges.

This is precisely why it is so important to distinguish between knowledge, skills and experience when it comes to matching, recruiting and hiring, as each dimension brings unique value to the process. When recruiting candidates, an organisation must consider the specific requirements of the job or position being filled. For example, if an organisation is recruiting for a technical role, proven knowledge/applied skills in a specific coding language and programming may be more important than knowledge of theoretical concepts related to the field.

While knowledge and skills are essential, experience provides the most valuable and relevant insight into the job and the field. For example, a candidate with superb but more theoretical programming skills may not be the best fit for a role if they lack relevant work experience. It is also important to balance the different dimensions in an evidence-based matching and hiring process. Some organisations may place more emphasis on technical skills, while others may focus on soft skills such as communication and teamwork. Therefore, organisations need to have a clear understanding of the specific requirements of the role and the desired qualifications and determine the weighting of knowledge, skills and experience accordingly. At the same time, especially organisations with a strong focus on just hard or job-related skills should not overlook factors such as attitude, cultural fit and potential for growth. These factors can play a significant role in predicting long-term success and retention.

Therefore, a balanced matching and hiring process that considers all these dimensions holistically can help organisations to identify the best candidates for their open positions. Neither an exclusive focus on background, education and thus knowledge, nor on the skills or soft skills that are so widely propagated today, will enable accurate and sustainable artificial or human intelligence-based matching results and thus successful hiring. Let’s start dealing with these terms and dimensions in a more differentiated way, it would only benefit us all.

In recent years there have been many posts, articles, and reports on how AI and automation will shape the future of work. Depending on the author’s perspective or agenda, these pieces go one of two ways: either the new technology will destroy jobs and have devastating effects on the labor market, or it will create a better, brighter future for everyone by destroying only the boring jobs and generating better, much more interesting ones. ChatGPT, for instance, has spurred unprecedented hype, swinging like a pendulum between extremes of opinions. On the one hand, there have been overwhelmingly euphoric responses, with people fabulating about how we could leverage this allegedly superintelligent technology to enhance our work – from journalism to coding and data analysis to project management or school assignments. On the other hand, many are voicing concerns about potential misuse, including ChatGPT writing malware code and phishing emails, spreading misinformation, disclosing private information, and replacing humans in the workplace. In this post, we want to take a more nuanced view by discussing the most common arguments and claims and comparing them with the facts. But before we get into this, let us first clarify what AI-driven digital transformation is. In a nutshell, it is all about automation, using AI technology to complete tasks that we do not want humans to perform or that humans cannot accomplish – just as we did in the past, in the first, second, and third industrial revolutions.

From stocking looms to AI art

With each of these revolutions came the fear that human workers would become obsolete. So why do we want to automate? Even though in some cases, inventors are simply interested in the feat of the invention itself, more often than not, an invention or development is driven by business interests. As is widespread adoption. And no matter which era, businesses rarely have other goals than staying competitive and raising profits. 16th-century stocking looms were invented to increase productivity and lower costs by substituting human labor. Steam-powered machines in 19th-century mills and factories and farm machinery were used for the same reason. Robots in vehicle manufacture in the second half of the 20th century – ditto. Whether the technology is tractors, assembly lines, or spreadsheets, the first-order goal was to substitute human musculature with mechanical power, human handiwork with machine consistency, and slow and error-prone “humanware” with digital calculation. But so far, even though many jobs were lost to automation, others have been created. Massively increased production called for jobs related to increased distribution. With passenger cars displacing horse-powered travel and equestrian occupations and increasing private mobility, jobs were instead created in the expanding industry of roadside food and accommodation. Increasing computational power used to replace human tasks in offices also led to entirely new products and the gaming industry. And the rising wealth and population growth accompanying such developments led to increased recreational and consumption demands, boosting these sectors and creating jobs – albeit not as many as one may think, as we will see below. However, we cannot simply assume that the current revolution will follow the same pattern and create more jobs and wealth than it will destroy just because this is what happened in the past. Unlike mechanical technology and basic computing, AI technologies not only have the potential to replace cheap laborers, say, with cleaning or agricultural robots. They have also begun outperforming expensive workers such as pathologists in diagnosing cancer and other medical professionals diagnosing and treating patients. These technologies are now also taking on creative tasks such as creative writing, choosing scenes for movie trailers, or producing digital art. We should not necessarily assume the extreme of a dystopian future with fewer jobs and sinking wealth. But we must keep in mind that, in many cases, it is currently more cost-effective to replace expensive workers with AI solutions than cheap laborers such as textile workers in Bangladesh.

So, working towards a more differentiated view, let us look at the currently most common claims and how they stand up to closer scrutiny.

Claim 1. AI will create more/fewer jobs than it destroys

This is the main argument put forth in utopian/dystopian scenarios, including reports by WEF (97m new jobs vs. 85m displaced jobs across 26 countries by 2025), PwC (“any job losses from automation are likely to be broadly offset in the long run by new jobs created”), Forrester (job losses of 29% by 2030 with only 13% job creation to compensate) and many others. Either way, any net change can pose significant challenges. As BCG states in a recent report on the topic, “the net number of jobs lost or gained is an artificially simple metric” to estimate the impact of digitalization. A net change of zero or even an increase in jobs could cause major asymmetries in the labor market with dramatic talent shortages in some industries or occupations and massive worker surplus and unemployment in others. On the other hand, instead of causing unemployment – or at least underemployment, fewer jobs could also lead to more job sharing and shorter work weeks. Then again, although this may sound good in theory, it raises additional questions: How will pay and benefits be affected? And who reaps the bulk of monetary rewards? Companies? Workers? The government? It is admittedly too soon to see the effects of widespread AI adoption on overall employment or wages. But past outcomes, i.e., of previous industrial revolutions, do not guarantee similar outcomes in the future. And even the past ones show that job and wealth growth were not necessarily as glorious as often portrayed. The ratio of employment to working-age population has risen in OECD countries since 1970, from 64% to 69% in 2022.[1] However, much of this increase can be attributed to higher labor participation rates, especially among women. And the increased wealth is certainly not evenly distributed, e.g., in the US.

 

Sources: Illustration 1: Economic Policy Institute, https://www.epi.org/publication/inequality-2021-ssa-data/; Illustration 2: Author’s calculations based on data from Economic Policy Institute, State of Working America Data Library, “Wages by percentile and wage ratios,” 2022.  Updated March 2023
*Low wage is 10^th percentile, middle wage is 50^th percentile, very high wage is 95^th percentile. No data available for 2020-2022 in the 95^th percentile because of issues with significant shares of the population facing BLS’ top-code value for weekly wages.

 

There are simply no grounds to assume that AI and automation will automatically make us wealthier as a society or that the increased wealth will be distributed evenly. We should thus be equally prepared for more negative scenarios and discuss how to mitigate the consequences. For instance, would it be acceptable to treat AI processes like human labor? If so, we could consider taxing them to support the redistribution of wealth or to finance training or benefits and pensions for displaced workers.

In addition, we should question any job displacement estimates on a basic level. Who can confidently say that this job will decline? How can we know what kind of jobs there will be in the future? None of these projections are truly reliable or objective – they are primarily based on some group of people’s opinions. For instance, the WEF’s Future of Jobs Report, one of the most influential reports on this topic, is based on employer surveys. But it is naive to think anyone, let alone a cadre of arbitrary business leaders, can have a confident understanding of which jobs and skills will be required in the future. One should not expect more from this than from fortunetelling at a fair. Just look at the predictions about cars in the early 19th century, remote shopping in the 1960s, cell phones in the 1980s, or computers since the 1940s. So many tech predictions have been so utterly wrong – why should this change now? And yet, these types of crystal ball predictions are a key element in the estimates for the “future of work”.

The fact is, scientifically sound research on this topic is extremely scarce. One of the few papers in this area studied the impact of AI on labor markets in the US from 2007 to 2018. The authors (from MIT, Princeton, and Boston University) found that greater AI exposure within businesses is associated with lower hiring rates, i.e., up until now, AI adoption has been concentrated on substitution rather than augmentation of jobs. The same paper also finds no evidence that the large productivity effects of AI will increase hiring. Some may say that this supports the dystopian view. However, we must also note that this study is based on online vacancy data, and thus the results should be treated with caution, as we explained in detail in one of our other posts. In addition, due to the dynamics of technological innovation and adoption, it is almost impossible to extrapolate and project such findings to make robust predictions for future developments.

And on a more philosophical side note, what would it mean for human existence if we worked substantially less? Work is ingrained in our very nature; it is a defining trait.

Claim 2: Computers are good at what we find hard and bad at what we find easy

Hard and easy for who? Luckily, we do not all have the same strengths and weaknesses, so we do not all find the same tasks “easy” and “hard”. This is just yet another extremely generalizing statement based on completely subjective judgment. And if it were true, then most people would probably consider repetitive tasks as typically easy, or at least easier. This directly contradicts the next claim:

Claim 3: AI will (only) destroy repetitive jobs and will generate more interesting, higher-value ones.

The WEF states that AI will automate repetitive tasks like data entry and assembly line manufacturing, “allowing workers to focus on higher-value and higher-touch tasks” with “benefits for both businesses and individuals who will have more time to be creative, strategic, and entrepreneurial.” BCG talks of the “shift from jobs with repetitive tasks in production lines to those in the programming and maintenance of production technology” and how “the removal of mundane, repetitive tasks in legal, accounting, administrative, and similar professions opens the possibility for employees to take on more strategic roles”. The question is, who exactly benefits from this? Not every worker who can perform repetitive tasks has the potential to take on strategic, creative, and entrepreneurial roles, or program and maintain production technology. It is simply a fact that not everyone can be trained for every role. More satisfying, interesting tasks for intellectuals (such as the advocates of a brighter future of work thanks to AI) may be too challenging for a less intellectual worker whose job – which may well have been perfectly satisfying to them – has just been automated. And not every white-collar worker can or wants to be an entrepreneur or strategist. Also, what exactly does “higher value” mean? Who benefits from this? The new jobs created so far, like Amazon warehouse workers, or Uber and Postmates drivers, are not exactly paying decent, secured living wages. And since the early 1970s, businesses have demonstrated a clear disinterest in sharing the added value from productivity gains with workers:

 

Source: Economic Policy Institute, https://www.epi.org/productivity-pay-gap/

 

On the other hand, a vast number of the AI applications that are already available perform higher- to highly-skilled tasks based on data mining, pattern recognition, and data analysis: diagnosis and treatment of medical conditions, customer service chatbots, crop optimization and farming strategies, financial or insurance advising, fraud detection, scheduling and routing in logistics and public transport, market research and behavioral analysis, workforce planning, product design, and much more. Now add the manifold applications of ChatGPT into the mix. The full effect of these applications on the job market is not yet clear, but they are certainly not only removing mundane, repetitive tasks from job profiles.

Claim 4: We (just) need to up-/reskill workers.

While we certainly do not disagree with this statement in general, it is often brought up as a more or less simple remedy to prepare for the future AI-driven shifts in the labor market and “embrace the positive societal benefits of AI” (WEF). The fact is, this comes with several caveats that make it a far from simple solution.

First, we cannot repeat enough that it is impossible to predict the “future of work” reliably, especially which jobs will be in demand and which will not. Also, based on the effects of the previous industrial revolutions and current research, it is highly likely that the widespread adoption of AI will introduce new jobs with profiles that we cannot anticipate yet. This means we must equip current and future professionals with the skills necessary for jobs we currently know nothing about. One commonly suggested workaround to this issue is to encourage lifelong learning and promote more adaptable and short-term forms of training and education. That is certainly a valid and increasingly popular option. However, there are several aspects to keep in mind. For instance, 15–20% of the US and EU adult population[2] have low literacy (PIAAC level 1 or below), which means they have trouble with tasks such as filling out forms or understanding texts about unfamiliar topics. How can these people be trained to succeed at “more complex and rewarding projects” if they cannot read a textbook, navigate a manual, or write a simple report? In addition, around 10% of full-time workers in the US and EU are working poor.[3] These people typically have neither the time, resources, nor support from employers for lifelong learning and thus no well-informed access to efficient, targeted, and affordable (re)training.

By the time such issues have been addressed, many of these workers may have already missed the boat. In 2018, US employers estimated that more than a quarter of their workforce would need at least three months of training to keep pace with the necessary skill requirements of their current roles by 2022.[4] Two years later, that share has more than doubled to over 60%, and the numbers are similar worldwide.[5] In addition, even before the post-Great Recession period, only roughly 6 in 10 displaced US workers were re-employed within 12 months in the 2000 to 2006 period.[6] In 2019, this rate was the same in the EU.[7] With increasingly rapid changes in skills demands, combined with a lack of time and/or resources for vulnerable groups such as the working poor and workers with low literacy, not to mention lacking safety nets and targeted measures in underfunded workforce development systems, the prospects for these workers are unlikely to improve.

Moreover, the pandemic massively accelerated the adoption of automation and AI in the workplace in many sectors. Robots, machines, and AI systems have been deployed to clean floors, take temperatures or food orders, replace employees in dining halls, toll booths, or call centers, patrol empty real estate, increase industrial production of hospital supplies, and much more within an extremely short time. In the past, new technology was deployed gradually, giving employees time to transition into new roles. This time, employers scrambled to replace workers with machines or software due to sudden lockdown or social distancing orders. This is a crucial difference from the preceding industrial revolutions. Many workers have been cut loose with not enough time to retrain. Similarly disruptive events may well occur in the future – be it another pandemic or a technological breakthrough – and as a society, we need to be prepared for these events and provide affected workers with swift, efficient, and, above all, realistic support.

Claim 5: Employers should view up- and reskilling as an investment, not as an expense.

If a company replaces all its cashiers with robots, why would it want to reskill the newly redundant workers? Even governments have a hard time taking this stance on training and education. Many countries focus primarily on college or other training for young workers rather than retraining job seekers or employees. For instance, the US government spends 0.1% of GDP to help workers navigate job transitions, less than half what it spent 30 years ago – despite skills demands changing much faster than it did three decades ago. And the vast majority of businesses are primarily interested in maximizing profits – that is just how our economy works. Remember: we live in a world where even sandwich makers and dogwalkers may be forced by their employers to sign noncompete agreements to prevent them from getting a raise by threatening to move to a competitor for higher pay.

Well-performing conversational software could enable a company to take a 1,000-person call center and run it with 100 people plus chatbots. A bot can respond to 10,000 queries in an hour, far higher than any realistic volume even the most efficient call center rep could handle. In addition, a chatbot does not fall ill, need time off work, or ask for perks and benefits. They make consistent, evidence-based decisions and do not steal from or defraud their employers. So, if the quality of this software is sufficient and the price is right, there would probably be an uproar amongst shareholders if a company did not go for this offer. After all, a solution that increases efficiency and productivity while lowering expenses is a company’s dream come true. So, if this company doesn’t opt for it, its competition will. And despite the “tech for social good” propaganda we constantly hear from Silicon Valley, most companies are not interested in the future of soon-to-be-ex workers.

Beyond the bubble

The bottom line is that we cannot afford to overdramatize or simply reassure ourselves that there will be enough jobs, or we will constantly be playing catch up. Most of the commonly cited problems or solutions tend to be discussed within the academic or high-income bubble of researchers, tech entrepreneurs, and policymakers, mixed with a substantial amount of idealism. But to get ahead of these developments that – for good or for bad – have vast potential to completely transform our labor markets and society, we need to look beyond our bubble and design realistic strategies for the future based on facts and objective data.

 

[1] https://stats.oecd.org/Index.aspx?DatasetCode=LFS_SEXAGE_I_R#

[2] US: https://www.libraryjournal.com/?detailStory=How-Serious-Is-Americas-Literacy-Problem
EU: http://www.eli-net.eu/fileadmin/ELINET/Redaktion/Factsheet-Literacy_in_Europe-A4.pdf

[3] US: https://nationalequityatlas.org/indicators/Working_poor?breakdown=by-race-ethnicity&workst01=1
EU: https://ec.europa.eu/eurostat/databrowser/view/sdg_01_41/default/table?lang=en

[4] The Future of Jobs Report 2018, World Economic Forum, 2018.

[5] The Future of Jobs Report 2020, World Economic Forum, 2020.

[6] Back to Work: United States: Improving the Re-employment Prospects of Displaced Workers, OECD, 2016.

[7] https://skillspanorama.cedefop.europa.eu/en/dashboard/long-term-unemployment-rate?year=2019&country=EU#1 

“If I had an hour to solve a problem, I’d spend 55 minutes thinking about the problem and five minutes thinking about solutions.” – Albert Einstein

 

This is the second in a series of posts on machine learning in HR tech. If you haven’t already, we recommend you read the first post here.

In our last post, we explained why it takes more than data science and machine learning (ML) to build a knowledge graph for a job matching system. In a nutshell, to build a knowledge representation of sufficient quality, you need people who understand the knowledge you want to represent. More generally, if you want to solve a problem as opposed to putting out fires, you need people who understand the problem (and not firefighters). This is key not only to determining a good approach, but to the entire system lifecycle. So many attempts at innovative problem-solving fall flat because a) they target the wrong problem or b) the people involved did not understand the problem. So, before we get into the nitty-gritty of ML-based HR tech in the next post, let’s discuss the problem we want to solve, starting with the all-important results.

The jobs

The discussion that follows is based around a job matching system as an example of HR tech. However, this discussion is highly relevant to any kind of HR analytics.

In a job matching system, at the most superficial level, you want to feed candidate and job profiles into a machine, let the machine do its magic and spit out a list of… well, depending on the problem you want to solve, it could be:

• a list of candidates that best match a job description
• a list of jobs that best match a candidate
• a list of candidates similar to a given candidate
• a list of jobs similar to a given job
• …

A significant part of understanding the problem is defining similarity. What makes one job similar to another? A first idea might be that two jobs are similar if they have similar or even the same job titles. So, when are job titles similar? Take a look at the following examples:

 

 

The job titles on the right all share the keyword Manager, but they are clearly not similar in any respect. On the other hand, most of the job titles on the right are similar in some sense. However, whether they are close enough depends on the problem you want to solve, and where the expressions came from. For instance, if you want to use the results of your system for, say, national statistics, these job titles may be close enough. If you want to recommend jobs to a software engineer, that person may not be interested in any of the other jobs, as they only cover a subset of the tasks and skills of a software engineer – in theory. In reality, you will find that the terms software engineer, software developer and software programmer are often confused or used interchangeably in job postings. In addition, even identical job titles often refer to very different jobs: an accountant for a global conglomerate is not the same as an accountant for a small local business; a project manager in the manufacturing department may not perform well if transferred to the marketing department; a carpenter with 18 months of training is probably not as skilled as a carpenter with 4 years of training. Business size, industry, training and many other factors all impact the skill set required or acquired in a given position. So let’s consider jobs in terms of skills.

The skills

We discussed skills in quite some detail in this post. The gist of it is that first of all, there is a lot of talk about skills, but no consensus on the most basic and pertinent question, namely what exactly constitutes a skill, i.e. the definition. Then there is the question of granularity, i.e. how much detail should be captured. The choice is highly dependent on the problem you want to solve. However, your system will typically need to understand skills at the finest level of granularity in any case so that it can correctly normalize any term it encounters to whatever level of detail you settle on. Which leads to the final point: When it comes to skills, we have a highly unfavorable circumstance of projection bias in a tower of babel. Given a term or expression describing a skill, most of us expect that everyone means the same thing when they use that term. In reality, there is significant confusion because everyone has their own interpretation based on their unique combination of experience, knowledge and other factors. We also discussed this in an episode of our podcast: Analyzing skills data. Long story short: There is much work to do in terms of skills and skills modeling.

Now, in an ideal world everyone would speak the same skills language. Realistically, this is simply not going to happen. What we can do is attempt to integrate this translation work within our system. Which is one of the key strengths of a knowledge representation. And we discussed in the last post of this series at length why a good knowledge graph cannot be generated by a system purely based on ML. So let’s suppose for a moment we have a solid definition of skills, the appropriate level of granularity for our problem and we are all talking about the same thing.

Again, we want to find a workable definition of similarity. Working with the premise that a job is determined by its skill set, if you have two jobs with the same skills, then the jobs are the same. This implies that the more skills two jobs have in common, the more similar the jobs. At the other end of the spectrum, two jobs that have no skills in common are not similar. Sound’s logical, right? So, if we feed lots of job postings into our system to analyze them all in terms of skills, our system can identify similar jobs. Easy. Let’s look at an example. The following are all required skills listed in three real-life job postings:

 

 

According to our theory – and with an understanding that certain terms have similar meanings, like client centered and guest focus – jobs A and B must be similar and job C quite different. Think again.

 

 

Ok, but surely our system will discern more accurate and comparable skill sets if we feed it more data. Well, it might – if your system processes that data correctly. So far, these systems typically deliver results like the ones discussed in this post, where 1 in 15 online job postings for refuse workers in Europe apparently require knowledge in fine arts.

The more pertinent question, however, is how useful that would be to the problem you want to solve. As we have explained before, there is no such thing as a standard skill set for a given profession in a global sense. In addition, standardizing skills sets according to job titles is more or less equivalent to simply comparing job titles. So, if you have a matching or recommendation system, this approach will not be helpful. If you want to perform skills analysis, say, to develop a skills strategy for your business, it will not be helpful either. Instead, we need to come up with a viable definition of job similarity that not only consists of a set of features like job titles, skills, industries, company size, experience, education, and so on. The definition must also include aspects such as the importance of each feature depending on the context. For instance, specific training and certification is an indispensable feature of jobs for registered nurses and irrelevant for stocking and unloading jobs at a supermarket. Work experience is not helpful in matching graduates to entry-level jobs, but likely necessary when looking or hiring for a management position. Of course, if you’re designing a purely ML-based system, you probably want to leave the task of determining importance or weighting of the features to the system. However, somewhere along the line, you will (hopefully) want someone to check the results. And it shouldn’t be a data scientist.

The system

Suppose you have found a workable definition of job similarity and you have a good knowledge graph to help your system understand all the different terms used in your job-related data. Now you want to build a matching engine based on ML. Again, you need to first think about the problem at hand. Apart from the semantic aspects discussed above, there are ethical considerations. One thing you will want to avoid for obvious reasons is bias – in any use case. Also, if you want the system to be used in any part of an HR tech stack, it must be both explainable and interpretable. For one thing, there is increasing evidence that legislation will eventually dictate explainability and interpretability in ML-based HR tech (AI Bill of Rights, EU AI Act, Canada’s AIDA, and so on). 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. But first, we need to discuss what exactly we mean by interpretability and explainability in an ML-based system, and how the two differ.

These terms are often used interchangeably, and the difference between the two is not always clear. In short, an ML system is interpretable if a human can understand exactly how the model is generating results. For instance, a matching engine is interpretable if the designers know how the various features (job titles, skills, experience, etc.) and their weights determine the match results. An explainable ML system can tell humans why it generated a specific result. The difference is subtle, but key. Think of it like this: Just because the system told you why it did something (and the explanation sounds reasonable), that doesn’t mean that you know why it did it.

In our matching engine example, an explainable system will provide a written explanation giving some insight into what went into the decision-making process. You can see, say, a candidate’s strengths and weaknesses, which is certainly helpful for some use cases. But you don’t know exactly how this information played into the matching score and thus you cannot reproduce the result yourself. In fact, depending on how the system is designed, you can’t even be sure that the explanation given really matches the actual reasoning of the system. It is also unclear whether the explanation accounts for all factors that went into the decision. There could be hidden bias or other problematic features. Apart from the fact that these issues could cause difficulties in a legal dispute, it looks a lot like transparency washing—particularly because most people are unaware of the potential inaccuracy of these explanations.

In an interpretable system, you know precisely how the results are generated. They are reproducible and consistent, as well as accurately explainable. In addition, it is easier to identify and correct bias and build trust. Simply put, an interpretable ML system can easily be made ethical by design.

However, even in an interpretable system you still face the challenge of correctly processing the data. It doesn’t really matter what color the box is, black, white or any other color under the sun. It’s still opaque if your system can’t properly deal with the input data. Because mistakes in the processing inevitably lead to spurious reasoning. To perform any kind of reliable analysis, your system must – at the very least – be able recognize and normalize the relevant information in a dataset correctly. Otherwise, we just end up back where we started:

 

 

Of course, no system is perfect, so the question is how much trash are you willing to accept? In other words, you need benchmarks and metrics. You need experts that are actually capable of evaluating the system and the results. And maybe let’s not set those benchmarks too low. After all, being better than appalling isn’t saying much…

As a fan of ML and data science (which, by the way, we are too – as long as it’s put to good use), you may still want to build a purely ML-based machine that can correctly process the data, feed it into an interpretable system and produce fair and accurate results with truthful explanations that can be understood by humans. So, in the next post, we’re going to dive into the nuts and bolts of this ML-based machine, looking at everything from training data over natural language processing and vector embeddings to bias mitigation, interpretability and explainability. Stay tuned.

Read the last article in our series on current events in the labor market, written from the perspective of an airport employee. We conclude it by turning to price pressure and the associated issue of consumer responsibility, and illustrate this with the example of the struggling airline industry. In doing so, we also show that this sector and its troubles are ubiquitous and should be of interest to all of us – not just because of its impact on the environment.

 

Dear readers,

Over the past few weeks, I have followed with great interest (and often also a dash of horror) the reporting by various media channels on the ongoing crisis situation in the aviation industry that is caused by a shortage of staff. Even though I did not always agree with everything that was said – especially in the comments sections – I feel that it is very important that this matter is being addressed publicly. (Disclaimer: I myself have been working as a baggage handler in this very industry for many years, have a lively exchange with my work colleagues at the airport and am therefore speaking from actual experience). In said news contributions, I have read, seen or heard almost everything, from management salary analyses to insurance tips for travelers. However, one, in fact obvious, aspect of the whole mess seemed to be missing in the conversation. You may have already guessed it, I’m referring to the role of consumers, who coincidentally (?) often also belong to the target group of cited media. In the following, I would therefore like to say a few words on this subject. Before you now dismiss it, because you do not feel addressed as a non- or non-frequent flyer, I would like to add this preliminary remark: My experience in the airline industry is just one instance of a problem that extends far beyond into other areas. No matter whether we talk about aviation or the import of cheap goods. The luxury of some people always comes at the expense of others. And be aware that we look at this very fact through the lens of the rich developed nation of Switzerland – what it means for other countries, most people do not even want to imagine.

One more preface: I would really like to thank those who publicly speak out for better working conditions and wages in my industry. This verbal support is an important start to improving circumstances in sectors and professions like mine. But it is also just that; verbal and a start. Is it laudable to use your voice to stand up for workers who due to the holiday season have other things to do than argue about long waiting times in the comments? Or to say thank you to them? Absolutely. But unfortunately, at the end of the day, I still get very little out of it if my salary  is based on the (still applicable) crisis collective wage agreement and allows me to live just above the poverty line. In addition, the authors of such statements lose a great deal of sincerity and credibility in my eyes when in real life you still end up encountering them hunting for the cheapest bargain or loudly proclaiming their annoyance over a lost suitcase. It’s like “I really do think that those poor bastards who carry my luggage deserve more. But…”. As soon as they get to feel the pain themselves, solidarity comes to an abrupt end for many people. Even better, of course, are those who don’t even think about people like me and just think that “it’s so cool” to party every weekend in a different European city…

In the current discussion about aviation, you can hear a lot of moaning about the mismanaging, greedy executives of the airlines and airports. Believe me, I probably agree with you on many points there. Of course, a general price increase will not guarantee an automatic positive impact on employees’ wages. But this does not change the fact that most people are not willing to pay higher prices for their flights in the first place – not always just because they could not afford it. After all, I’ve already stumbled over the word “human right” several times in relation to flying nowadays. “Fun” fact from the TV program Kassensturz: In order to cover all the costs actually incurred, air ticket prices would actually have to be at least twice as high. These actual costs refer to the circumstance that the airline industry still benefits from a number of outdated special rights, resulting in reduced costs: Unlike for cars, for example, there is no mineral oil tax for airlines. And unlike train tickets, no value-added taxes are levied on plane tickets.

If the calls for fairer wages from those outside the industry are meant to be sincere, the real cost of air travel in the broader sense should also include the almost universally mediocre pay of many employees in my industry. Since the beginning of the 1980s and the worldwide deregulation of commercial aviation, flying has become steadily cheaper, so it is no wonder that today’s consumers have become very sensitive to cost increases – they are not used to anything other than low-cost carriers. But they should be (or become) aware that the price pressure they induce is often passed on unfiltered to the staff in the air and on the ground. As I said, there can’t only be winners in such equations. Just as a small example: A cabin crew member in Switzerland earns between CHF 3,145 and 4,500 gross, depending on seniority. After social deductions and health insurance, this is hardly enough to live on, especially if you are not allowed to live more than an hour away from the airport because of your job. Let alone to provide for old age. It’s therefore not surprising that it’s becoming increasingly difficult to find people who will work under such conditions. Clearly, it is also up to those “price-sensitive” customers to recognize their responsibility for the consequences of their demands and to redefine their point of view. Not to mention the hypocrisy of certain passengers who claim to environmentally compensate their flight to Bali with a Meatless Monday and some fair fashion…

And one more thing: As is already becoming apparent, it is not only the airline industry’s environmental impact that ultimately affects us all. What the situation at many international airports is showing us right now is that stingy behavior that is neither ecologically nor economically sustainable will ultimately backfire. Sooner or later not only the ‘direct losers’ in the equation will have to bear the resulting costs, but the general public as well. Be it in the form of delays, lost luggage or when taxpayers’ money has to be used to pay the health insurance premium subsidies of underpaid stewardesses and the national pension scheme has ever larger funding holes due to the inadequate salaries. We are also already seeing what the consumers’ reaction to the cancelled flights can bring: many people switch to driving by car (not to trains that would have been more climate-friendly) and traffic jams are currently forming the likes of which we have not seen for a long time. Now whether that’s better, you tell me.

That is why, ladies and gentlemen, I plead for more consumer (co-)responsibility, also in the case of aviation. Of course, it is easier and more pleasant to deny any involvement in problematic developments and to point fingers at others or to describe everything as a structural problem (and therefore unsolvable by the individual). But this does not resolve the issue and is of limited help to the employees directly affected by the crisis. The scenario is similar to that in other sectors of the economy; just think of the food service sector or the fast-fashion paradox. In my opinion, being unaware (or is it calculated ignorance?) does not fully shield you from complicity, and as long as I continue to overhear passengers getting upset about the smallest increases in fees and prices, I cannot take their appreciation and compassion 100 percent seriously. Therefore, dear consumers, please do care, because you will feel the effects of the current mess not only during the next strike.

Mario V., Reader [1]

 

At JANZZ, it is not only important to us that the best job candidates, regardless of location and industry, get the best match with an advertised position, but also that they are compensated appropriately for their work. This is one of the many reasons why we are a trusted partner to an ever-growing number of Public Employment Services (PES) in various countries around the world. We develop evidence-based solutions and have been successfully deploying them since 2010. Our job and skills-matching solutions are fair and non-discriminatory and provide completely unbiased results in accordance with the OECD principles on AI.

Would you like to take a step towards fairer labor markets? Then contact us at info@janzz.technology, via our contact form or visit our product page for PES.

 

[1] Mario V. is a fictional character from our series on current events in aviation and their impact on the labor market. See here for Part 1 and Part 2. Any similarities with real persons, living or dead, are purely coincidental.