JANZZ ontology – empowering your data and realizing smart applications

Ontologies have been around in artificial intelligence (AI) research for the last 40 years.[1] Just as trends come and go, ontologies too have had their ups and downs. Introduced in the 80s, ontologies became popular in the mid-90s. After machine learning (ML) came on the scene in 2000, the widespread opinion was that in the future every task performed with a computer (by means of AI and ML) could be solved with a smart algorithm. A lot of companies invested heavily in these algorithms hoping to have the next breakthrough in AI.

With the rapid development of AI and ML, especially after the emergence of the convolutional neural networks (CNN), the core technology – deep learning (DL) – has been growing rampantly in terms of parameter size and computing complexity. Today some of the most complex models have reached a scale of billions of parameters. [2]

Still, concerns have been raised regarding the current mainstream DL. Let us take supervised learning in image recognition as an example: the images used to train the AI models need to be manually identified in terms of the position and contour of the target objects, for the models to be able to find implicit pattern features between the data after comparing different labeling results. If we recall how we learn things as infants, us human beings can easily identify and classify different objects without needing this kind of instruction. [2]

DL methods have made tremendous progress and are now able to extract knowledge from the training data. However, this knowledge is not explicitly explainable, because the so-called “black box” training cannot reveal the complex relations hidden within the models. When facing new problems, the current DL models are unable to apply their acquired knowledge to solve new challenges in an effective way. [2]

There is another big concern regarding big data and the privacy issue related to it. What is more, current DL methods are based on big data which is not applicable to industries that generate small amounts of data such as certain fields in medicine and human resources. This case requires AI systems to have the ability to reason and judge, which can only be successful in specific domain areas. [3]

Many powerful ontologies already exist for specific domains, examples include the Financial Industry Business Ontology (FIBO) as well as numerous ontologies for healthcare, geography or occupations. It is widely believed that knowledge integration and DL are the important ideas for further amplifying the effectiveness of DL. For this reason, ontologies have made it back into the spotlight, along with many equivalents such as knowledge graphs or knowledge representations.

At JANZZ.technology, we started to build our ontology – JANZZon! – in 2008, before the tech giant Google invented and popularized the term “knowledge graph”. JANZZ.technology has been building its ontology using domain experts with various backgrounds (e.g. intellectual property law, fluid dynamics, car repair, open-heart surgery, or educational and vocational systems).

Today, JANZZon! is the largest multilingual encyclopedic knowledge representation in the field of occupation data. The main focus lies on jobs, job classifications, hard and soft skills, training/qualifications, etc. The number of stored nodes and relations comes to more than 350 million!

Integrated with both data-driven and expert consultation taxonomies, JANZZon! covers ESCO, O*Net, ISCO-08, GB/T 6556-2015, DISCO II and the UK skills taxonomy from Nesta just to name a few. Currently, 9 languages (German, English, French, Italian, Spanish, Portuguese, Dutch, Arabic and Norwegian) fully cover occupation, skills, specializations, function, education, etc., and we are working on achieving the same level in a total of 40 languages in 2020.

Being the backbone of our job and skills matching technology, JANZZon! represents the knowledge at the deepest level where all the entities have been encoded and vectorized in the semantic space. Therefore, when searching and matching, our technology can truly understand a concept and its semantic meaning and thus guarantee meaningful results.

If you are wondering how ontologies can help you empower your data in the fields of human resources and labor markets and aid you in realizing smart applications? Please write to sales@janzz.technology

 

[1] ODSC. 2018. Where Ontologies End and Knowledge Graphs Begin. URL: https://medium.com/predict/where-ontologies-end-and-knowledge-graphs-begin-6fe0cdede1ed [2019.11.20]

[2] Li Jun. 2019. Shen Du Xue Xi: Xin Shi Dai De Lian Jin Shu. URL : https://www.ftchinese.com/story/001084827?page=1&archive [2019.11.20]

[3] Cai Fangfang. 2019. Qin Hua Zi Ran Yu Yan Chu Li Ke Xue Jia Sun Maosong: Shen Du Xue Xi Peng Pi Zhi Hou, Wo Men Hai Neng Zuo Shen Me? URL: https://www.infoq.cn/article/OvhfhpPChTLpsMgrf43N [2019.11.20]

The potential of AI in human resource management

Artificial intelligence (AI) is unquestionably a powerful tool. Its economic value is increasing tremendously and transforming numerous industries such as manufacturing, fintech, healthcare and automobile. Workers in finance and marketing have much success using AI technologies, whereas HR practitioners find it rather hard to integrate these into their daily practices.

Prasanna Tambe, Peter Cappelli and Valery Yakubovich state in their research: “there are systemic and structural differences for HR that do make it harder, when you are building an AI-based system.” [1]. Due to the fact that the quality and explanatory power of big data and AI are limited they are still considered unconventional in the fields of HR and employment. To have a better understanding of this matter, we need to consider the AI problems in terms of data science in human resource management (HRM).

There are three main challenges in HR practices when it comes to data science. The first issue is the lack of consistency in HR process measurement throughout the employee life cycle. For example, when determining which candidate to hire or choosing whom to promote, it is crucial to consistently record and analyze which criteria and skills were the decisive factors in the previous hiring process.

The second problem with HR practices is the limitation of data sets produced in HRM. Unlike some fields such as marketing and finance, where a lot of data are generated and easily gathered, data collection in HRM faces big challenges in terms of quantity and quality. Moreover, data in HRM is oftentimes unstructured (on paper, in excel or PDF) and consequently, difficult for a computer to process.

The last difficulty concerns ethical issues related to data processing. The results of HR decisions can have a significant impact on someone’s career. Therefore, it is imperative to think about how fairness and transparency can be achieved. Furthermore, it is also crucial to know how employees react to the results that are solely based on data-driven algorithms. As Morgan Hampton from Tesla declared, “recruitment should be automated as much as possible, hiring should remain human.”

Taking these three issues into consideration when searching for AI solutions, HR managers should focus on the following aspects in order to utilize AI more effectively. Firstly, HR managers need to create the right HR process that is ready both for the digital age and AI technology.

Currently, AI technologies are separately, for example in recruiting and talent acquisition, payroll management and self-service transactions. However, they lack a mechanism to generate data that can assist the whole AI process in HR practices.

HR managers often merely keep the applications that they are interested in and don’t retain those that are screened out. This leads to a one-dimensional analysis and conclusion [1]. All these criteria should be gathered in data collection and eventually, be evaluated to facilitate the development of big data models and AI processes.

Furthermore, it is also imperative to generate data in a sustainable way. For instance, there are AI applications that can predict which workers are about to quit their jobs, and some even track data points from employees’ social media or emails [2]. If employees were aware of such a system, they would probably change their behavior and deliberately produce misleading data.

Last year, the story about the Amazon AI recruiting tool being biased against women was proof that machine learning can mimic human attitudes. Gender, nonetheless, is not the only aspect that is reason for discrimination. Others such as age, nationality or ethnicity could also have a negative impact, keeping companies from inclusive and diverse hiring. HR managers should carefully collect data samples that are representative and look for explainable AI solutions. The complex neural networks in deep learning is far from self-explanatory.

Up until today, the standard data criteria that HR managers should respect throughout the HR practice cycle does not exist yet. This means that HR managers have to team up with their company’s internal IT department or with external AI vendors to determine what data to trace and how to measure those data, in order to establish the best practices for AI within their companies.

At JANZZ.technology we believe that collecting and structuring data is fundamental for creating smart data. Our parsing tool extracts the right entities from paper, Excel or PDF, ensuring a fair end-to-end data processing from the very beginning. Do you want to know more about our parser and how we can assist you in your AI transformation journey? Please write now to sales@janzz.technology

 

 

[1]Prasanna Tambe, Peter Cappellli and Valery Yakubovich. 2019. Artificial intelligence in human resources management: Challenges and a path forward. URL:https://www.researchgate.net/profile/Peter_Cappelli/publication/328798021_Artificial_Intelligence_in_Human_Resources_Management_Challenges_and_a_Path_Forward/links/5c5edc7f299bf1d14cb7dc5f/Artificial-Intelligence-in-Human-Resources-Management-Challenges-and-a-Path-Forward.pdf [2019.10.20]

[2] Samantha Mclaren. 2019. Here’s how IBM predicts 95% of its turnover using data. URL:https://business.linkedin.com/talent-solutions/blog/artificial-intelligence/2019/IBM-predicts-95-percent-of-turnover-using-AI-and-data[2019.10.20]

Sex segregation in the workplace

The segregation of people in the workplace according to their biological sex is partly due to different preferences and aptitude for specific occupations. Traditionally, jobs with the highest concentration of women are to be found in teaching, nursing and other care-related service work. The majority of male workers, conversely, holds blue-collar jobs, for instance in construction, equipment operation or repairing.

Furthermore, since occupation fields dominated by female workers have a lower compensation in comparison, it is more common for women to change into a profession that is male dominated than vice-versa. This is accompanied by recent developments such as the #MeToo movement and the introduction of women’s quota. However, this change is one-sided: occupations with a disproportional amount of men such as childcare remain largely unchanged.

In 1996, the European Commission Network on Childcare set the strategic goal to increase male employment in childcare to 20% by 2020. With a few months to go before the deadline, the employment rate of men in this domain is still far below the target number.

In Germany currently 6 in 100 pre-school childcare workers are male. In the UK and Ireland, the numbers are even lower than 2 out of 100. Norway, which is considered the global leader when it comes to gender equality, has the highest rate – with 9 in 100 men working in early childcare. This score is nevertheless far below the target of 20%. Outside of Europe the situation remains fairly unchanged. In the United States and Australia, the number of men working in early childcare amount to 4% and 2%, respectively.

Interestingly, things are quite different in a childcare center situated in Stuttgart, Germany. This center employs 12 people, half of which are male; moreover, it keeps receiving applications from more men. What is the secret of this center that appears to be so attractive for male childcare workers?

Mr. Nöth, the center’s founder and a former childcare worker explains that, unlike in traditional childcare centers with fixed groups, in his center children are presented with seven different activity groups, including ones for painting, handcraft and gymnastics. Each group activity takes place in a separate, specially equipped room and the children can attend a different group every day. Nöth notes that this way of operating offers opportunities for the childcare employees to work creatively, which includes individually designing and developing the activities. Such liberties, in turn, are said to be highly valued by many men. [1]

Gender stereotyping in the workplace like the one pertaining to men in early childcare is still present and it can create a hostile environment for male employees. In addition, work life can also be isolating when most co-workers are female. If there is little camaraderie and social activities among male coworkers, this might impel the few men to quit. At least, this is suggested by the fact that several of the male childcare workers from the Stuttgart center report to feel more comfortable among the relatively high number of male coworkers.

Such special cases can give important indications for changing the workplace situation where sex segregation poses a significant challenge. One point is concerned with creating new functions for roles that are dominated by one particular sex.

This is already happening in some blue-collar professions. With the advent of new technologies some of the jobs are shifting their focus from intense physical tasks towards sophisticated machine-involving activities. This in turn attracts an increasing number of women to the field.

Another point involves the achievement of gender balance in the workplace. If one sex is in the minority it is likelier to have unpleasant experiences at work. Statistics indicate that for women such unpleasantness increases tremendously in occupations with over 90% male employees.

In Norway, the education system actively directs men towards certain teacher colleges in order to prevent unbalanced concentration. This way, it encourages more men to apply and contributes to higher shares of male workers in early childcare. [2]

For almost a decade, JANZZ.technology has been observing and working with many labor markets worldwide. Our unique matching solution only uses parameters that truly matter for job matching. This includes functions, skills, specialization, experience etc. but avoids biases with regard to age, gender or origin. To learn more about our solutions please write now to sales@janzz.technology

 

 

 

[1] Philipp Awounou. 2019. Kann Mann machen. URL: https://www.spiegel.de/karriere/kita-in-stuttgart-wo-das-halbe-personal-maennlich-ist-a-1281251.html [2019.10.03]

[2] Jack Graham. 2018. Men don’t feel welcome in early childhood. Here’s how to change that. URL: https://apolitical.co/solution_article/men-not-welcome-gender-inequality-in-the-early-childhood-profession/ [2019.10.03]

JANZZ.technology offers explainable AI (XAI)

Over the past decade, thanks to the availability of large datasets and more advanced computing power, machine learning (ML), especially deep learning systems, have experienced a significant improvement. However, the dramatic success of ML has forced us to tolerate the process of Artificial Intelligence (AI) applications. Due to their increasingly more autonomous systems, current machines are unable to inform their users about their actions.

Nowadays, most AI technologies are made by private companies that make sure to keep their data processing a secret. Furthermore, many companies employ complex neural networks in AI technologies that cannot provide an explanation on how they come up with certain results.

If this kind of system, for example, wrongly affects customers’ traveling, this might not be of great consequence. However, what if it falsely impacts autonomous vehicles, medical diagnoses, policy-making or someone’s job? In this case it would be hard to blindly agree with a system’s decision-making process.

At the beginning of this year, the Organization for Economic Cooperation and Development (OECD) put forward its principles on AI with the purpose of promoting innovation and trustworthiness. One of the five complementary value-based principles for the responsible stewardship of a trustworthy AI is that “there should be transparency and responsible disclosure around AI systems to ensure that people understand AI-based outcomes and can challenge them.” [1]

Explainable AI (XAI) has recently emerged in the field of ML as a means to address the issue of “black box” decisions in AI systems. As mentioned above, most of the current algorithms used for ML cannot be understood by humans in terms of how and why a decision is made. It is hence hard to diagnose these decisions for errors and biases. This is especially the case for most of the popular algorithms in deep learning neural network approaches. [2]

Consequently, numerous regulatory parties, including the OECD, have urged companies for more XAI. The General Data Protection Regulation, which took effect in Europe, provided the people in the European Union with a “right to a human review” of any algorithmic decision. In the United States, insurance laws force companies to elaborate on their decisions such as why they reject the coverage of a certain group of people or charge only a few with a higher premium. [3]

There are two main problems associated with XAI. Firstly, it is challenging to correctly define the concept of XAI. Furthermore, users should be aware of what the limitations of their knowledge are. If companies had no choice but to provide detailed explanations for everything, intellectual property as a unique selling proposition (USP) would disappear. [4]

The second problematic factor is assessing the trade-off between performance and explainability. Do we need to standardize certain tasks and regulate industries to force them to search for transparently integrated AI solutions? Even if that means putting a very high burden on the potential of those industries.

At JANZZ.technology, we try our best to explain to our users how we match candidates and positions. Our unique matching software excludes secondary parameters such as gender, age, or nationality and only compares skills, education/training, specializations, experiences, etc. It only uses aspects that truly matter in order to find the perfect candidates.

Instead of giving one matching score, our unique matching system breaks down all criteria such as functions, skills, languages and availability. This allows users to have a better understanding of the results and sets the foundation for reskilling and upskilling the workforce to be analyzed. Do you want to know more about the ways in which JANZZ.technology applies explainable AI solutions? Please write now to  sales@janzz.technology

 

 

[1] OECD. 2019. OECD Principles on AI. URL :https://www.oecd.org/going-digital/ai/principles/ [2019.9.17].

[2] Ron Schmelzer. 2019. Understanding Explainable AI. URL: https://www.forbes.com/sites/cognitiveworld/2019/07/23/understanding-explainable-ai/#6b4882fa7c9e[2019.9.17].

[3] Jeremy Kahn. 2018. Artificial Intelligence Has Some Explaining to Do. URL: https://www.bloomberg.com/news/articles/2018-12-12/artificial-intelligence-has-some-explaining-to-do[2019.9.17].

[4] Rudina Seseri. 2018. The problem with ‘explainable AI’. URL: https://techcrunch.com/2018/06/14/the-problem-with-explainable-ai/[2019.9.17].

 

 

 

 

In China’s industrial heartland city of Chongqing, a smart technology upgrading is on full display

The second Smart China Expo (SCE) was successfully held during 26-29 August in Chongqing China. Some of the most newsworthy Chinese business leaders, including Jack Ma Yun (Founder of Alibaba Group), Pony Ma Huateng (Chairman and CEO of Tencent), Robin Li Yanghong (Co-founder, Chairman and CEO of Baidu), Yang Yuanqing (Chairman and CEO of Lenovo Group), gathered in Chongqing  and shared their views on intelligent technology, offering their suggestions for the smart industry.

What is more, President of the People’s Republic of China, Xi Jinping, sent a congratulatory letter for the opening ceremony of the SCE and Chongqing was announced to be the permanent meeting place of this high standard event. But what makes this city the pioneer of the intelligent and smart industry in China? Perhaps the following aspects could give you some insights.

 China’s strategic development    

At the end of the 20th century and the beginning of the 21st century, the Central Committee of the Communist Party of China and the State Council carried out Deng Xiaoping’s thought of “two overall situations” in China’s modernization drive and made a strategy for the development of the western region. As the only municipality directly under the Central Government in Western China, Chongqing has ushered in historic opportunities and accelerated the pace of municipality development.

In 2013, the Chinese government took the “One Belt One Road” initiative as a global strategy, and Chongqing once again welcomed the golden opportunity for development. As the intersection of “Silk Road Economic Belt” and “Yangtze River Economic Belt”, the strategy of “One Belt One Road” accelerated Chongqing’s opening to the East and West.

After stepping on the express train to achieve high economic growth, Chongqing’s economy encounters downward pressure. In order to achieve a new round of growth, since 2018, Chongqing has implemented an innovation-driven development strategic action plan led by the intellectualization of big data.

Complete industrial chain   

Chongqing’s choice was clearly well thought out. Chongqing plays an important role in the integration of informatization and industrialization in China. It is one of the country’s oldest industrial bases in the West and an important industrial manufacturing base for the country. Additionally, the industry in Chongqing has got a relatively complete chain. Besides the traditional manufacturing industry, it also includes the electronic information and communication industry.

It is China’s largest manufacturer of cars and is home to some of the largest car manufacturers such as “Changan Ford” and “LIFAN”. It also produces one in every three laptops in the world. Having a good industrial foundation makes it feasible to transform and upgrade the industrial economy with smart technology.

 Well established digital platforms   

After several years of efforts, Chongqing has built a unified information sharing platform for the entire city. A three-level (country-city-district) government information resource sharing system links the national platform and 38 district platforms together.

Chongqing has taken the lead in completing data sharing at the provincial/municipality level, through which the isolated information islands of the departments can be bridged, and the data aggregation can be accelerated. More than 200 applications for government services, urban planning, urban governance, etc. within 80 departments have been realized.

Well established network infrastructure

Chongqing has become one of the 10 first-level nodes in the national communication network architecture, which have greatly enhanced Chongqing’s position as an internet hub in the West and the supporting capacity of Chongqing’s network infrastructure.

Chongqing has also become one of the two big cities in the Western Region where three operators (Mobile, Unicom and Telecom) have launched 5G pilot projects together. Thus far, Chongqing Mobile has opened two 5G base stations. Within this year, it plans to build and open 50 5G base stations in the main urban area.

 During the opening, the newest technologies, products and applications in the fields of artificial intelligence, big data, self-driving vehicles, drone and virtual technology, and so on, were presented and released. The exhibitors included several technology heavyweights such as Alibaba, Google, Inspure, Qualicomm, Huawei and Baidu.

Among the 843 companies from 28 countries and regions, JANZZ.technology was honored to take part in the SCE in Chongqing. We believe that China will be the leading country in smart technology in the future and we hope to seek potential Chinese partners that can help us open the door to the Chinese market. We could still recall the hundreds of youngsters we met during the expo. Through their excited eyes, we see the bright future of the city.

Where is the next generation of craftsmen in Switzerland? (And elsewhere?)

According to the latest Nahtstellenbarometers – Education Decisions after Compulsory Schooling, published by Innovation SBFI and State Secretariat for Education, Research and Innovation (SERI), the most desired professions for vocational training in Switzerland are commercial employees, followed by healthcare assistants and information technologists. This means that none of the professions in handicraft are among the top 5. Compared to boys, there are 15% less girls aspiring to follow the vocational training, and not one single girl reports to be interested in choosing an apprenticeship as electrician, as a car mechanic or as a polytechnical technician. [1]

There is currently a deficit of 42,778 handicraftsmen in Switzerland. This figure was published by a job search website that also pointed out that craftsmen jobs form one of the most advertised job category in years: currently, there is a total number of 198,097 vacancies advertised. Companies in construction, and building and dwelling services are struggling to hire skilled workers. Stefan Danev, Managing Director of an electrical and safety engineering company in Winterthur, ZH, says that “finding personnel to fill our vacancies in the long term is not easy under the current market situation”. [2]

 Growing interest in the academic route

What causes this shortage of craftsmen in Switzerland? The increasing popularity of the academic route is mentioned as a contributing factor for this development. Between the ages of 12 and 14, Swiss adolescents have to decide on going to pre-university baccalaureate schools or searching for apprenticeships offered by companies (this is known as the dual-track system).

Having a long tradition, the dual-track system was well received by parents, by adolescents and by society at large, as it has largely contributed to a very low youth unemployment rate and high levels of workers’ competency and of quality in all kinds of skilled trades in Switzerland.

However, a growing number of parents hold the perception that the vocational system has a lower quality and that students who enter vocational schools are less capable or have lower aspirations compared to students with four-year university degrees. This is especially the case among parents with migration background. International companies, too, add pressure by not recognizing graduates from vocational training as equal to graduates with a bachelor’s degree. This, in turn, forces a lot of young people to hesitate about pursuing vocational training.

Recently, there has been an increase in adolescents choosing the baccalaureate track, especially in the French and Italian parts of Switzerland. This poses a direct challenge for some of the professions in skilled trades. SERI noted in a statement that “across Switzerland, the baccalaureate has gained more interest and the desire for a general education is stronger than last year” [3].

Misconceptions in skilled trades

When looking at the media, one could think that the only prosperous professions in the future are computer-science-related, and that most skilled-trade jobs—especially handicraft—will be replaced by automation and robots. But under certain circumstances, machines do not yet have enough dexterity and fine motor skills to compete with human hands. For example, to grind bearings that has accuracy within a 100th of millimeter needs years of practice and only experienced skilled craftsman can do that and sometimes other than experience, it also required intuition which machines will never have.

One tends to have the misconception of skilled trades as providing little chance of climbing up the career ladder, and, hence, of offering a small salary. But the reality is that there are many top executives in big companies, as well as well-known government figures in Switzerland who have worked their way up from apprenticeship. Highly qualified craftsmen are particularly in demand today and their salary is likely to increase in the near future.

Moreover, the job provides the opportunity to become an entrepreneur. Unfortunately, there are many people in the skilled trades business who intend to pass on their very successful and lucrative business but cannot find a willing successor. Apart from the fact that few young people are trained in such business, it is also reported in various surveys that Generation Y/Millennials (born between 1980-1995) are seeking more security and flexibility in work and prefer having a good, stable salary in an international company, instead of taking on the responsibility of entrepreneurship.

Are young people getting too lazy?

In addition to Switzerland, many other countries like Germany, Belgium, Austria and the United States are facing similar shortages of craftsmen. It is an undeniable fact that the nature of many skilled trades jobs is hard, and that there is a general trend among parents who were in those professions to push their children towards more comfortable jobs. For example, in an apprenticeship at a bakery, one has to start the job at 2am, a tough job that many are unwilling to take up nowadays.

One hardly hears about a child saying that his/her dream job is to become a boilermaker, a metal worker or a welder, because few of them have the opportunity to get introduced to such professions at an early age. With no understanding of or even misconceptions about such jobs, more and more young people are likely to be discouraged from starting an apprenticeship in such professions.

Christoph Thomann, president of the Central Board of Vocational Education and Training Switzerland, believes that the skilled trades are outshined by an increased focus on computers and robots among young people [1]. However, digitalization should not only be regarded as a barrier from choosing vocational training. In fact, vocational training should embrace digitalization and promote an image of the digitalized craftsman, a modern and less traditionally masculine image that attracts more young people, even young girls, to the field.

A collaborative effort

As in the other economic sectors, digitalization has also made its way into skilled trades and is having a profound impact by increasing the productivity and reducing business costs. For example, by using data analysis, roofers can now measure a house with a 3D scanner to order the exact number of roof tiles needed.

Building information modeling (BIM) helps to realize the integration of building information, which ranges from the design, construction and operation of the building to the end of the whole life cycle of the project. A lot of different information is integrated into a three-dimensional model information database for the stakeholders. There are of course many more examples, including virtual reality, drones, 3D mapping and 3D printing.

Despite great efforts in raising awareness of the importance of the professions in skilled trades communities, reality is disappointing. Matthias Engel from the Swiss Building Association appeals to the federal government in Bern by saying: “It is important that politicians do not send the wrong signals. It is important that the government not only promotes secondary and university education, but also vocational training”.

Indeed, a greater effort to actively promote vocational training, diminishing the gap between young men and women and encouraging more young women to take apprenticeships in certain professions ought to be made by the government. Furthermore, it needs to be considered to bring in young people from other countries than Switzerland, if necessary.

What can AI and big data do for contractors, headhunting firms specializing in skilled trades jobs and government agencies planning to promote vocational training?  How can you use digital platforms to hire the people with the skills you need? Write now to sales@janzz.technology and let JANZZ.technology help you with our intelligent data.

 

[1] gfs.bern. 2019. Nahtstellenbarometer 2019. URL: https://cockpit.gfsbern.ch/de/cockpit/nahtstellenbarometer-2019/ [2019.07.25]

[2] Ulrich Rotzinger and Julia Fritsche. 2019. In der Schweiz fehlen 42’778 Handwerker. URL: https://www.blick.ch/news/wirtschaft/in-der-schweiz-fehlen-42778-handwerker-schreiner-sanitaere-und-elektroinstallateure-verzweifelt-gesucht-id15399544.html?fbclid=IwAR0TU2tUpeSmljN23gLwK5S09DOpvURnFdNqBNoR6nRSfLo_Z3ChojKrVYE [2019.07.25]

[3] Isobel Leybold-Johnson. 2019. What careers did Switzerland’s students choose this year? URL: https://www.swissinfo.ch/eng/continuing-education_what-careers-did-switzerland-s-students-choose-this-year-/45035674 [2019.07.25]

Middle-skilled workers to be hit hardest by digitization

While low-skilled workers are going to suffer the most from the consequences of digital transformation long-term (with some exceptions), opportunities for middle-skilled jobs are shrinking the most, according to recent observations in OECD countries.

We used to talk about digitalization and automatization only as processes that will change our working environment in the future, for example through the replacement of humans by robots. Meanwhile, the situation has changed: many of us already feel the effects of digitization and automation. These effects are likely to be amplified even further in future.

Due to the energy revolution that digitalization induced, many companies in the energy business, blindsided by the speed of this revolution, are faced with overcapacity. In Switzerland, General Electric (GE) has just announced a major workforce reduction, which causes 450 employees in Baden and Birr to lose their job. In order to compete with international online providers Migros, one of Switzerland’s biggest retail companies, too, undergoes such transformation. In June, the Genossenschaft Migros Ostschweiz released the dismissal of 90 employees in Gossau. The Organization for Economic Cooperation and Development (OECD) predicts difficult times for Swiss employees: 700,000 jobs are associated with a “high risk of automation.” [1] Moreover, this is only a small, partial reflection of the whole, global labor market.

It is difficult to account for the full impact of digitalization since it bears both positive and negative effects for the job market. Statistical evidence however indicates that digitalization affects the distribution of work, income and wages. [2] With skills like problem-solving, creative thinking and complex communication that are complementary to digitization, high-skilled workers tend to benefit the most from digitalization. As a result, we can observe an increase in high-skilled jobs in most OECD countries. Likewise, the share of low-skilled jobs grew while the share of middle-skilled jobs decreased. [3]

Why are middle-skilled workers at greatest risk to be disadvantaged under digitalization? Martin Wörter, Professor of Innovation Economics at ETH Zurich explains that “repetitive activities in the office or in industrial production can be replaced more easily by computers or robots.” Federal employment statistics for Switzerland back up this statement. Within 20 years the number of office workers decreased by 150,000 while the number of craftsmen fell by 90,000. Conversely, the number of academic professions grew by 470,000. [1]

However, it is short-sighted for companies to simply lay off unqualified workers and to replace them with employees who fit the demanded skills profile. Since skill requirements are changing faster than ever, even if companies could replace their unqualified workers today, what about tomorrow? The only way to solve this problem is to enable reskilling of the existing workforce. Further training could largely reduce redundancies and benefit the company at large. As Bruno Staffelbach, Professor of Human Resource Management and President of the University of Lucerne, says: “Company-specific know-how will become even more important in the future. However, employees can only acquire these skills on the job in their company.” [1]

Many companies have realized this and adopted effective skills development programs.  However, as we have written before on this site, workforce reskilling requires an ecosystem approach that involves individuals, companies, industries, as well as governments. Based on calculations by the World Economic Forum, in the US 45% of workers at risk could be collectively reskilled through businesses working together. If combined with governmental efforts, this number could increase to even 77%. [4]

For almost a decade, JANZZ.technology has been watching and collaborating in many labor markets worldwide. We offer our know-how and the right data on skills and specializations to tackle general challenges in the job market. Our latest product, Labor Market Dashboard uses real time data in order to establish important labor market indexes such as the most required skills, the most searched for positions or the female/male ratio. Should we have caught your interest and should you wish to learn more about JANZZ.technology’s offers, please write now to sales@janzz.technology

 

 

[1] Albert Steck. 2019.Digitalisierung gefährdet Jobs von Mittelqualifizierten am stärksten. URL: https://nzzas.nzz.ch/wirtschaft/digitalisierung-gefaehrdet-jobs-von-mittelqualifizierten-am-staerksten-ld.1492570#swglogin [2019.07.02]

[2] OECD. 2015. OECD skills outlook 2015: youth, skills and employability, OECD Publishing, Paris, URL: https://doi.org/10.1787/9789264234178-en [2019.07.02]

[3] OECD. 2019. OECD skills outlook 2019: thriving in a digital world, OECD Publishing, Paris, URL: https://doi.org/10.1787/df80bc12-en [2019.07.02]

[4] Borge Brende. 2019. We need a reskilling revolution. Here’s how to make it happen. URL: https://www.weforum.org/agenda/2019/04/skills-jobs-investing-in-people-inclusive-growth/ [2019.07.02]

Occupational classification systems in the digital age

People have long been monitoring the economic activities of our society. It is said that during the Chinese Tang Dynasty (618-907) there were 36 different job types. Fittingly, the period marks the origin of the famous Chinese saying that ‘every trade has its master’ (san shi liu hang, hang hang chu zhuang yuan).

Today, jobs are changing at such a speed that it is almost impossible to give an exact number of the occupations that affect our daily life. Compiling statistical records of occupations is also becoming complicated since jobs are changing, disappearing and emerging. There used to be only ‘the’ manager, but now there is a PI manager, an IT manager, a project manager, an intergenerational engagement manager, you name it.

Thus, other than listing simply all occupations for statistical purposes, job descriptions, skill and experience requirements, education levels and more aspects are integrated, too, in occupation-related databases. That way, we can not only better understand the jobs of today but also develop more sophisticated systems that are able to perform more complex services with occupation data. For example, this enables performing the tasks of career planning, job searching, identifying trends or guiding policy design.

US-based classification systems

The United States Department of Commerce released the Standard Occupational Classification (SOC) in 1977. Back then, many programs by the US government began collecting statistics which is why the federal government needed a unified occupational classification system. SOC entails a short description and illustrative examples for each job. It is classified based on the type of work performed, but rarely on the level of skills and education needed for a specific position [1]. The latest version of SOC was published in 2018.

 The online database O*Net is an expansion of SOC and was created during the mid-1990s by the US Department of Labor’s Employment and Training Administration. O*Net can be freely accessed and downloaded by job seekers, students, businesses researchers and workforce development professionals alike. Compared to SOC, it is a much more sophisticated system with more detailed information such as tasks, technology skills, knowledge, abilities, education level and work style.

 Europe-based classification systems

The international Standard Classification of Occupations (ISCO) is maintained and managed by the International Labour Office (ILO). ISCO is the main international classification of occupation-related data and used for international exchange, reporting and comparison. It also serves countries and regions that want to either further develop their own occupational classifications or directly adapt one from ISCO-08. Examples include Ö-ISCO in Austria, Styrk-08 in Norway, COCR-2011 in Costa Rica, NOC 2016 in Canada and most national occupational classifications in Asia.

In July 2017, the European Union launched the first version of a European multilingual classification of skills, competencies, qualifications and occupations (ESCO) that is also based on ISCO-08. ESCO aims to create a common understanding of occupations, skills, knowledge and qualifications across the EU’s official 24 languages that enables employers, employees and educational institutions to better understand needs and requirements. Under freedom of movement ESCO could aid in making up for skill gaps and unemployment in the different member states, as the President of the European Commission Jean-Claude Juncker states [2].

Industry classifications

Industry classifications or industry taxonomies group companies by industry and in terms of production processes, products or job positions. They serve national and international statistical agencies for the analysis, comparison and summarization of economic conditions. Well-known industry taxonomies include NAICS, ISIC, GICS, NAF 2015 and MUPCS.

Furthermore, a shift from occupational classifications towards skills classification has been observed. This shift is linked to an attempt of improving classifications’ ability to aid in career guidance and the conduction of upskilling and reskilling. The United Kingdom and the innovation foundation Nesta have built the UK’s first data-driven skills taxonomy. It allows for measuring the country’s supply and demand of skills and for preventing skill shortages. The social media platform LinkedIn has also built a skills taxonomy for its users.

Chinese classification systems

China started to create its occupational classification in 1995. After four years, the country released its first version. Currently in use is a version from 2015 that aims to keep pace with the fast-changing employment sector. The Chinese classification has 4 digits with 1838 professions in total.

Compared to O*Net, which was created during about the same time period, there is still much room for improvement in the Chinese occupational classification. Specifically, it could be improved with regard to accessibility, continuous data updating and the provision of guidance for students and job seekers [3]. However, the problem of lacking in updated data is not unique to the Chinese occupational classification. This issue is shared with many other classifications, including O*Net.

A new concept for occupational classification systems

The creation of a traditional expert consultation taxonomy is time-consuming, costly and, most importantly, will lack the ability to continuously adapt to the world’s fast-changing working environment.  Therefore, a new solution is needed. One that can inform the labor market constantly and make job seekers, students, education providers, employers and policy-makers alert for change and empowered to react.

With digitalization, a data-based information collection methodology can revolutionize the way classification systems are created. At JANZZ.technology, we have mapped all international occupational classification systems and others in our ontology. (If you would like to learn about the difference between taxonomy and ontology, please check https://janzz.technology/ontology-and-taxonomy-stop-comparing-things-that-are-incomparable/).

This mapping allows us to analyze complex sets of occupational data and to annotate it with intelligent and standardized meta-data, which makes the data comparable in further processes like benchmarking, matching or statistical analyses. Our JANZZclassifier! is a product for everyone who has large volumes of (unstandardized) occupation-related data such as job titles, hard and soft skills and, particularly, training/qualifications. It enables you to simply run your data through our API and will return more meaningful data and, if desired, one of the standard classifications.

Above all, we are using real-time data, both from our users, our partners and the labor market in order to constantly update our database. It is the new way to develop classification systems in the digital age. Please write now to sales@janzz.technology if you wish to learn how our ontology may assist you.

[1] Jeffrey H. Greenhaus and Gerard A. Encyclopedia of career development.

[2] ESCO (2015). ESCO strategic framework. Vision, mission, position, added value and guiding principles. Brüssel.

[3] LI Wen – Dong and SHI Kan. 2006. A brief introduction to the development of the U.S. national standard occupational classification system and its implications to China. URL: https://www.docin.com/p-1479318301.html [2019.06.24]

Ambitious working women may find it hard in Switzerland

Despite being ranked among the world’s most livable places in terms of living standard, education, and healthcare, Switzerland’s cities might still not be the most ideal residence for aspirational working women, especially not for ambitious working moms. According to the 2018 Gender Gap Report, Switzerland ranked 20th. It specifically showed a significant gap between women and men in the areas of economy and political participation.

In Switzerland, women make up only 33.9 percent of senior positions, which include managers, senior officials and legislators. This share has barely changed over the past ten years. The report furthermore shows an inequality in income between women and men, with an estimated average income of $53,362 for women and one of $76,283 for men. [1] One might link the wide income gap to the disproportional representation of women in higher-ranking or higher-paid positions, occupations and industries, but the reasons are more complex.

Gender inequality, a global issue

Gender inequality in the workplace is a global issue that does not correlate directly with a country’s level of development. Thus, Switzerland is an illustrative example among many others. Extreme cases include Japan and South Korea. South Korea, one of the largest economies in Asia, recently made the abysmally low 116th place out of 144 countries in the World Economic Forum’s gender equality ranking [2].

As a part of one of the most advanced, wealthy and democratic nations, Japanese women have always been kept on the fringes of both the economic and political stage. In a worldwide comparison of developed countries, they thus remain in an extraordinarily disadvantaged position. Last year’s news about Tokyo Medical University illustrates this even further: the university altered the entrance examination scores of female candidates. Since such a move will only lead to even fewer female doctors in Japan, this makes the country’s society appear regrettably backward.

 Protest by women

For Swiss women, however, many things have developed positively since the advent of the #MeToo movement and the introduction of women’s quota. In tech and other sectors, personnel consultants observe a trend towards increasing the proportion of women in the executive committees. Simone Stebler from the personnel consultancy Egon Zehnder says that “today, gender diversity is an integral part of the business plan of most companies.” [3]

The Swiss multinational investment bank UBS wants to occupy at least one third of its leadership positions with women. The bank’s gender equality officer says: “We would like to see the proportion of women rising, especially in leadership positions.” Similarly, the pharmaceutical giant Novartis employs nearly as many women as men in its research cadre. Other international companies such as Swiss Re and Axa could also be counted towards such exemplary Swiss employers. [4]

Balancing the competing interests of family and work

However, global experts indicate that for women, the reconciliation of work and family still poses a big challenge. Difficulties in achieving a feasible work-life balance forced many women in Japan and South Korea to leave their jobs. In Switzerland, too, the traditional model of women as caretakers of the domestic realm persists, which is why part-time work is still the norm for Swiss working moms. According to the Federal Statistical Office, 4 out of 10 women work full-time, compared to 1.7 out of 10 men [3].

Valérie Borioli Sandoz, from the union group Travail.Suisse for equality policy, believes that part-time work is the “least bad solution for the reconciliation of work and family. However, it can be a trap, since it is very difficult to escape from it.” [5] Although part-time jobs are a luxury for Swiss moms, they also hold them back from taking certain occupations and higher positions in the workplace.

 One of the main reasons is cultural

Experts call on innovative and open ways to shape our working environment, including more flexible working hours and the co-fulfillment of top management roles. Even with structural changes in companies, the problem of missing female presentation will not be solved easily.

The traditional image of women counts as one of the major reasons why women participate less intensively in official labor markets. Gender and labor market expert Professor Barbara Riedmueller (Freie University in Berlin) points out that women occupying top positions are more accepted in countries where family services are traditionally outsourced. The primary examples are the Scandinavian countries. Gender equality can only be achieved if women no longer have to depend on men. This, however, is a question of changing the culture of a given society. [3]

 Valuing traditionally female occupations

Although there has been great progress for the rights of women in several societies, women are still often found in the working fields of health care, childcare and maintenance, where salaries remain medium to low. By contrast, men dominate high-paid fields such as computer programming, engineering, and technical work.  However, it is reductive to argue that women should only be encouraged to take more top management roles, to break into the male-dominated high-income industries or to demand equal payment in order to shorten the income gap.

 Currently, there are over 6000 professional care vacancies in Switzerland. This number has increased by double the size compared to five years ago.[6] While we need to promote gender diversity in less-female-dominated occupations and industries, we also have to improve women’s salaries, work-life balance, training and further education in traditionally ‘female careers’. This is necessary to reassert the value of such careers and to motivate more young people (male and female) to choose them as a profession. Eventually, this will benefit the health care sector and, as a result, society at large.

This can only be achieved if we all agree to increase the healthcare expenditure in order to heighten the wages in professional care. This way, governments have the financial resources to change the current situation. They need to financially support traditionally under-paid jobs, since caretakers and similar positions’ contribution to society is absolutely crucial. However, the question is whether everybody is really willing to pay for this.

For almost a decade, JANZZ.technology has been observing and working with many labor markets worldwide. We offer our know-how and the right data on skills and specializations to tackle general challenges in job market. If you are interested, please write now to sales@janzz.technology

 

 

[1] Swissinfo. 2018. Swiss gender partiy ranking shows slow progress. URL: https://www.swissinfo.ch/eng/global-gender-gap-report-_swiss-gender-parity-ranking-shows-slow-progress/44624274 [2019.06.13]

[2] Heather Barr. 2018. South Korean women are fed up with inequality. URL : https://www.hrw.org/news/2018/06/14/south-korean-women-are-fed-inequality [2019.06.13]

[3] Franziska Scheven. 2019. Berufstätige Frauen haben heute gute Aufstiegschancen – aber nur, wenn sie Vollzeit arbeiten. URL: https://www.nzz.ch/panorama/frauen-und-karriere-nach-metoo-kinder-hemmen-die-karriere-ld.1455524[2019.06.13]

[4] Franziska Phister. 2019. Mann oder Frau: wer schaff es nach oben? URL: https://nzzas.nzz.ch/wirtschaft/geschlechterkampf-mann-oder-frau-wer-schafft-es-nach-oben-ld.1458619#swglogin#swglogin [2019.06.13]

[5] Jessica Davis Plüss. 2019. Mothers face double-edged sword in Swiss workplace culture. URL: https://www.swissinfo.ch/eng/international-women-s-day_mothers-face-double-edged-sword-in-swiss-workplace-culture/44801716 [2019.06.13]

[6] Albert Steck. 2019. Offene Stellen auf Höchststand. http://jobs.nzz.ch/news/6/arbeitswelt/artikel/421/offene-stellen-auf-hochststand [2019.06.13]

What you should know when choosing your AI recruitment software

In a report, Deloitte presents the evolvement of HR technology in four stages. The first stage describes the period during the 1970s and 1980s, when the main attention of software vendors was on systems that help HR managers make records. During the second stage between the 1990s and early 2000s, capabilities to support recruiting, training and performance controlling were developed. Around 2010, at the third stage, vendors started to offer cloud services and more user-friendly systems to engage with employees’ self-services.

The Deloitte report claims that today we are in the fourth stage of HR technology. In order to react to work environment microtrends, vendors have to design tools that are targeted for teams, individuals and networks and also enhance people’s productivity. At JANZZ.technology, we too think that an HR software should assist HR managers in being more productive and in focusing more on value-added tasks. For example, it should reduce the effort needed for mundane tasks such as screening thousands of candidates and should enable to focus more on value-added tasks such as interviewing candidates. We further believe that this is going to be achieved by means of Artificial Intelligence (AI).

AI for recruiting

Talent acquisition is undeniably one of the most important parts of corporation management. This makes the recruitment software market the most competitive and interesting market to observe. Governmental organizations like Public Employment Services (PES) are also actively seeking solutions to deal with this matter. According to Crunchbase, solely in 2018 recruiting software startups received over $ 600 million of VC finance. In the latest Market Map by HR Tech China, recruiting software vendors constitute the largest part of all HR technology vendors.

The offers made by vendors include the testing and assessment of candidates, background checks, video interviewing and many recruitment platforms. Since early 2017, AI has emerged in the recruiting process. The ensuing rise of recruiting software’s so-called AI capabilities could make any HR manager feel overwhelmed.

‘AI for recruiting’ is an emerging technology used in HR recruitment processes. It employs AI technology and mainly aims to reduce repetitive, time-consuming and banal tasks, which helps recruiters and hiring managers focus on value-added activities. 52% of talent acquisition leaders state that the hardest part of recruitment is the screening of candidates from a large applicant pool. [1] For instance, an AI recruitment software can screen thousands of applications and recommend the top 5 candidates in a blink. Thus, HR managers using AI for recruiting will have more resources to assess the top picks in-depth, which in turn heightens their chance to really find the best suitable candidate.

By contrast, the larger public uses the word AI vaguely and often even inaccurately. Many companies use ‘AI’ to describe their products in order to make them appear as ‘upgraded to the next level’. In most cases, this sort of product advertising promises too much. It is therefore extremely important to be able to evaluate both such products and their vendors. The process is similar to that of hiring: only if you have various ways to assess options, you can find the best one possible.

Evaluating AI for recruiting technology

How should HR avoid over-promised software when choosing from all the products? We have come up with three principles to go by when choosing AI recruitment software:

Principle I: Be aware of the bias within AI recruiting software

Last year, the story about the Amazon AI recruiting tool secretly being biased against women was a wake-up call for all of us: machine learning can be just as biased as human beings. Therefore, it is extremely important to focus on algorithmic fairness and transparency.

You must be aware of how the software processes personal information data such as date of birth, gender and nationality. Which factors does the software take into consideration when matching? And does the software ignore irrelevant factors?

Apart from algorithms you have to make sure that the software has representative training data. In the case of the Amazon AI recruiting tool, the tool turned against female applicants because, for over ten years, the company trained its computer models with resumes summited by male applicants. Hence, make sure you ask your software vendor how they deal with their data source.

Principle II: Be sure to test before you purchase

Before buying a car, you would certainly test drive it first. This rule applies to buying recruiting software as well. Since a recruitment software system is an expensive and long-term investment for your business, it is wise to complete a POC (proof of concept).  In this trial run you’ll find out if the software can really solve your prioritized problems, perform the promised functions and handle your data within the required scale and scope.

JANZZ.technology conducted a POC with an intergovernmental organization to find out if our solution can truly help them save time when finding suitable candidates. Our AI software competed against their HR team in matching candidates worldwide to their open junior intern positions. After screening over thousands of applications, they were pleasantly surprised by our results.

Most good software vendors offer free trials. It is important to prepare your test data well in order to get the most out of your practice and maximally optimize this process. One of the often-ignored aspects when choosing a recruitment software is the maintenance and support needed after the purchase. Only with constant updating, the software is able to develop in parallel with the fast-changing markets, customer requirements and ongoing digitalization. Don’t simply trust marketing lines such as “50 other leading companies from your industry are use our software” or “top 100 out of the top 500 companies are also using our software”.

Principle III: Taking the “soft skills” of an AI recruiting software into consideration

Just like assessing a job candidate by evaluating his/her skills and soft skills, an AI recruiting software has its skills and soft skills as well. We determine the hard skills of a software as functionality, accuracy, data security, speed, languages capability and similar highly valuated competences.

However, many might overlook the soft skills of an AI recruiting software. Soft skills of an AI recruiting software are the ability to deeply understand the language, education, working and social system etc. of your practice region, and the ability to localize certain regions or countries.

For instance, a country like Spain has more than one language: Castilian(Spanish), Catalan, Valenciana, Galician and Basque. A good AI recruiting software should be able to understand the different languages and know the common terms that are used in the four different languages to refer to exactly the same thing.

Conducting job matching across Europe is not an easy task, for instance because each of the 44 countries has its own education system (even with the bologna framework). It is an enormous amount of work to compare the different education levels and match people to jobs. Does your vendor have the right knowledge to solve such problems within your practice region?

Besides language and education, there are many more and equally important categories that need to be taken seriously.  As an international corporation operating in different countries, you want to have a software which understands all your markets.

Limitations of AI 

You may have heard of all the promised benefits of using an AI recruiting software. Before the AI can do its magic, we are here to give you a word of caution. Don’t expect the AI software to make the hiring decision on its own. Many of the previous use cases proved to us that the technology just is not ready yet. “How to make sure the algorithm is really interpretable and explainable – that is quite far off.” [2]

If you are expecting AI and the algorithm to do their job well, it is equally important for you to check your company’s or organization’s readiness for AI, in order to maximize its power. We all know that identifying patterns and making predictions requires a large quantity of data. With the widespread open-source algorithms, the real game changer is the data used to train the algorithms. Any business or organization should have a clear plan on how to generate the quantitative and qualitative data which will help your AI recruiting software get more accurate results, this is economically profitable for your business as well.

JANZZ.technology supplies AI solutions for your recruiting system and helps you find the right skills and talents. The ontology JANZZon! and the smart matching engine JANZZsme! make complex problems such as job and skills matching computable and completely change the way we handle skills and talent searching. The applications of JANZZ.technology are structured semantically, meaning occupations, specialization, function, skills and qualifications etc. are interlinked logically. The JANZZ.technology applications can deliver meaningful results for complex searches in real time and across multiple languages. Our applications are constantly fed with new data generated from our users, therefore they become more accurate over time. Let the tools of JANZZ.technology assist you in finding your best fit candidates.  For a demo, please write now to sales@janzz.technology

[1] ideal. 2019. AI for recruiting: A definitive guide for HR professionals. URL: https://ideal.com/ai-recruiting/ [2019.05.28]

[2] Jeffrey Dastin. 2018. Amazon scraps secret AI recruiting tool that showed bias against women. URL: https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G [2019.05.28]