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AI Decision Making in the Workplace

-- By IppeiKawase - 21 Nov 2020

Introduction

Artificial intelligence (AI) has been used in many ways in a wide range of areas. One of such fields is the workplace. Many employers now have adopted AI to recruit employees, assess their performance, and dismiss them. What issues arise from these changes in the workplace? This essay will explore how AI is used in the workplace, what issues lie in such use, and the legal challenges to address these issues.

How AI is used in the workplace

According to Cambridge Dictionary, AI is defined as “the use of computer programs that have some of the qualities of the human mind, such as the ability to understand language, recognize pictures, and learn from experience.” This illustrates that it is obviously not human but a human-like computer program. How do we, humans, use these computer programs in workplaces? One of such situations is recruitment. For example, employers decide what characteristics of candidates should be evaluated and develop algorism to select candidates who have such preferable characteristics for the employer.

Another example is that algorism itself decides such characteristics based on data which contains information of employees who performed well in the past. These algorisms enable employers to find and select suitable candidates more efficiently. Especially in the second example, it is often said that it leads to fair recruitment because it is a computer, not human, that decides the recruitment standard. These kinds of AI use can be seen in other situations; employers review and evaluate employees to decide their salary or promotion or dismiss them.

What issues arise from the use of AI in the workplace

In this part, I will explore issues of AI use in the workplace in general and look at the actual example in which such an issue appears in the form of a lawsuit.

Overview

The main issue is that the use of AI may lead to discriminatory or unfair treatment of employees, including potential employees. As for the first example of algorism above, because employers decide the characteristics of a desirable person before developing algorism, there is a possibility that such employers decide, intentionally or unintentionally, in an arbitrary way. As for the second example, although there is no human intervention in the process of deciding characteristics, if data on which algorism relied contains biased or limited information, the outcome of the decision is likely to be biased. In this case, people other than the employer cannot understand how the algorism works to conclude unless the employer makes it public and available to others. Also, the possibility is that even some employers who use such technology cannot grasp the mechanism of it, just as ordinary people have little knowledge of how smartphones and the Internet actually work despite heavily relying on them. This difficulty in understanding the mechanism of AI seems more significant than traditional technologies because AI itself has the ability to understand and learn.

Example - Uber Case

In fact, recently, we have seen the actual lawsuit resulting from such use of AI in the workplace. On 26th October 2020, a UK-based union, App Drivers & Couriers Union (ADCU), which represents four former Uber drivers, filed legal action in the Netherlands against Uber over the use of an algorithm to dismiss the drivers. They argue that the drivers are wrongly accused of ‘fraudulent activity’ as detected by Uber’s AI systems and that the dismissals violate General Data Protection Regulation (GDPR), specifically a provision to protect people from unfair automated decision making.‍

What legal challenges are to address issues

So what kind of legal challenges do we face now to address these issues? This part will examine how GDPR regulations address these issues with the example of Uber case above and point out some challenges which GDPR faces.

GDPR

Article 22 GDPR provides, “The data subject shall have the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects concerning him or her or similarly significantly affects him or her.” With regard to the term “based solely,” the European Data Protection Board (EDPB) explains that if human reviews and takes account of other factors in making the final decision, that decision would not be ‘based solely’ on automated processing. The EDPB further explains that you cannot avoid the Article 22 provisions by “fabricating human involvement.” In the Uber case, the plaintiff claims that Uber’s decisions are based exclusively on automated processing of personal data of the drivers and there is no significant human intervention, by showing the fact that the messages sent by Uber to the drivers contain largely standardized and very general texts. In contrast, Uber argues that the activities of the drivers have been assessed by their employees.

Challenges

As this case illustrates, I think that at least GDPR provides workers with legal tools to fight against certain types of AI systems adopted by employers. However, although the EDPB notes that “fabricating human involvement” cannot be an excuse to escape from Article 22, the standard for judgment of whether an employer fabricates human involvement is not clear. Considering that there can be human involvement with AI systems to a certain extent, it seems not so difficult to explain such involvement afterward. In addition, the argument regarding the messages sent by Uber seems not so persuasive because it is possible for employers to make their employees involved in the decision-making of dismissal and send messages whose content is the same as those automatically generated by AI.

Conclusion

At this moment, AI has been heavily incorporated into our society, including workplaces, but AI itself has not become independent and there is a certain kind of human involvement to a varying degree. While GDPR addresses legal issues arising from the use of AI in workplaces, we need to further clarify what is a decision made solely by AI, through examining individual cases in the future.

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