Fear of the machine, rage against the machine? Why we are so afraid of AI in recruiting (and what could be done about it)

A new study from Germany shows that the use of artificial intelligence (AI) in job application processes is widely rejected and generally stirs up negative emotions in potential applicants. There were also numerous objections raised by the respondents. Depending on the context, fears of programmed bias or the negligent handling of personal data may well be justified. In principle, however, it would be well possible to dispel many of these concerns if employers and software providers made more efforts to ensure transparency and explainability in the use of AI in recruiting. In addition, it would be useful to have more comparative studies on the respective performance of humans and machines in order to curb the obvious resentment towards AI in the HR sector. Since the use of artificial intelligence in the recruitment process will be unstoppable, we are going to break down the massive black box around HR systems and clarify which requirements have to be fulfilled for a more successful deployment of algorithms & co.

Fear of the unknown

In total, around 65% of the study participants associate negative things with the idea of AI in recruiting. Since this represents a clear majority, it is especially interesting to consider the underlying reasons for this result. About two thirds of all respondents show no trust in decisions made within a hiring process using AI. The biggest weakness cited is that an automated process is impersonal. At the same time, however, only a small minority (6.3%) realized any contact with AI in recruiting in the past – although this is already a reality in many places. [1]

Facts and figures directly taken from the study

All of these findings point to one central problem: As long as there is no transparency regarding the topic of artificial intelligence in HR, it is impossible to convince job seekers and employees of the benefits of such processes. The poor image of AI in the recruiting process is therefore primarily due to a fear of the unknown, which in turn manifests itself in two ways. Respondents do not seem to know exactly how AI-based decisions are made. Neither do they realize how these decisions will affect the actual application process and their chances of being hired.

The X factor in AI

It is up to both software companies and employers to clarify this so that candidates are more aware of where and how artificial intelligence is used in HR. There are still many recruitment tools on the market whose machine-learning-based results cannot be adequately explained, replicated, or corrected by the developers when needed. As a result, such black-box processes also deny applicants a genuine option of consent for the collection, processing, storage, and deletion of personal data, which can lead to serious legal problems. Thus, in many places, both the requirements of the EU’s General Data Protection Regulation (GDPR) and the Organization for Economic Cooperation and Development’s (OECD) AI principle of transparency are not being met.

The answer to this problem is provided by so-called explainable artificial intelligence, XAI for short. In recent years, it has established itself as a proven approach to break open the black box around systems based on deep learning and artificial neural networks. At JANZZ.technology, we have been working with such explainable models for quite some time and, thanks to their combination with ontology-based semantic matching, we deliver numerous powerful solutions for all HR and labor market management processes.  It is of great importance to us that we make our services easy to understand and provide customers with the necessary knowledge about the mechanisms and processes behind our technologies. Our matching tool JANZZsme!, for example, does not simply deliver a rather meaningless matching score between a candidate’s profile and the job posting. Rather, it dissects all criteria into sub-aspects such as skills, language skills or experience, which all have their own, visible score and explain the results in a comprehensible way for both applicants and employers.

A large number of respondents expressed their desire for a personal contact person for queries during the hiring process. As we can see, this demand can be met to a large extent by means of explainable technologies and transparent information about them on the part of HR departments. In response to the finding that AI-based processes in the application process are slandered as being impersonal, it should be noted above all that today, final decisions still lie with a recruiter and this will also remain the case in the years to come. According to our expertise and many years of experience, there still is no fully automated hiring process anywhere that completely excludes human intervention from the process. It is therefore understandable, but unfounded, to fear that you as an individual will be reduced to nothing more than a string of ones and zeros during the application process. Likewise, your soft skills profile will not be completely disregarded.

Human versus machine: A comparison

In fact, we should ask why there is such a desire for human influence in the recruitment process in the first place when, paradoxically, half of the respondents say they fear the embedding of human biases in AI programming. [1] Moreover, the few meaningful comparative studies available on the performance of humans and machines by no means indicate that the former make better decisions in application processes. Another advantage of XAI in the area of recruiting is therefore that with it we get a better picture of the actual performance of automated processes and can quantitatively compare these results with those of manual processes.

Allow us to outline a short example from one of our own use cases. The assignment was to conduct a comparative POC for an international organization to find the most promising candidates for their highly coveted junior positions. For comparison, the selection was also made by the experienced HR managers who usually perform this process “manually” every year for a period of several weeks. Key parameters to be considered when comparing the results included the avoidance of bias, achieving the highest possible efficiency and, of course, finding the most suitable candidates.

The result is likely to surprise a majority of participants from the study described at the beginning: Firstly, when using our APIs, matching tools and parsers the processing time was reduced to a fraction of that of the manual process (3 days vs. 12-14 weeks). Interestingly, a quick process was the third-most frequently mentioned criterion for a positive candidate experience in the study. [1] Secondly, there was no bias at all in JANZZ’s XAI-based decision-making, while the HR managers’ choices showed massive biases in the variables origin, gender, and language – not surprising, given the myriad forms of (unconscious) bias that shape the manual hiring process. To be sure, our replicable process based on binding criteria meant that the objectively best candidates were selected and those did not always automatically meet specific diversity and inclusion expectations. But even such requirements are scalable if desired and can then be applied in a rule-based and consistent manner, provided that this decision is also communicated transparently to applicants. Surprisingly, in the study only 14% noticed one of the main benefits of XAI-based matching. Namely that it makes it easier and more reliable to find a job that actually matches your skills, competences and education. [1] For this purpose, our technology is based on multilingual semantic matching. This approach provides a flexible solution to the problems posed by an increasingly heterogeneous composition of knowledge, terminology and information in CVs and job advertisements, in turn making the matching process a whole lot more efficient.

Facts and figures from the JANZZ POC

Overall, our comparison clearly shows results in favor of AI. In order to reduce the existing fears of many potential employees, it would undoubtedly be valuable to have evaluations such as ours conducted on a broader and more regular basis. Still, the following conclusion can already be drawn from what we know by now: In terms of bias in the application process, AI enriched with deep learning and an underlying knowledge-rich system definitely does not perform worse than humans (see also link in last section). On the contrary, it even brings potential advantages such as an expedited process and, above all, objectivity. Moreover, the use of AI in HR is already gaining ground at an unstoppable pace. That’s not surprising, we’d say. Or does anyone see an alternative to cope with the increasing participation rate and movement in the labor market? Due to all these facts, at JANZZ.technology we adhere to the principle “No artificial intelligence without human intelligence”. XAI-based systems provide indispensable help in the tedious and costly pre-processes of manual recruitment and allow human recruiters to focus on the essential; finding the top candidates.

The capabilities of AI for HR go far beyond the hiring process, as it can also be used in a company’s strategic workforce planning, for example. If you would like to learn more about our broad range of services or get information about what JANZZ.technology can do for your specific needs, please contact us at info@janzz.technology or via contact form, or visit our product page for an overview of all our solutions. We also invite you to listen to our podcast in which we talk about interesting, related topics. In the current episode, for instance, we discuss the distinction between systems that are “knowledge-lean” and those that are “knowledge-rich” – a crucial difference, if you ask us!

[1] IU International University of Applied Sciences. 2022. AI in Recruiting: Emotions, Views, Expectations. The Impact of Artificial Intelligence on the Candidate Experience. URL: https://www.iu.de/en/research/studies/ki-in-recruiting-study/