Just Hiring! How to counter discrimination in AI-assisted recruiting at the governance level. A toolkit for policymakers
Contains bibliogr. pp. 61-65
"In this Toolkit, we offer introductory information and concrete recommendations on how to mitigate the risk of discrimination in algorithmic hiring systems on a governance level. This Toolkit addresses policymakers and people working in regulatory bodies, as well as civil society organisations interested in this field, and intends to help them gain a better understanding of what they can contribute." (Page 4)
RECOMMENDATIONS
1 Governing AI in hiring is key: Key recommendations, 8
PART 1: WHY IS ALGORITHMIC DISCRIMINATION PROBLEMATIC?
2 Algorithmic discrimination in hiring is a lose-lose scenario that can't be tackled on a technical level only, 14
3 Discrimination can occur at each stage of the AI-assisted hiring process. Sources of bias and other challenges in addressing discrimination in algorithmic hiring, 17
4 Algorithmic discrimination in hiring is real - and significantly affects real people. Experiences of navigating in opaque hiring processes, 22
PART 2: WHAT DOES THE LAW SAY AND WHAT LEGAL LOOPHOLES DOES IT LEAVE?
5 Intersectional discrimination needs to be considered in policy and law. EU Non-discrimination law and its limits, 29
6 Algorithmic discrimination in hiring is difficult to challenge and needs better redress mechanisms. A fragmented landscape of current legal frameworks, 33
7 The AI Act could have done more to tackle algorithmic discrimination in hiring. The EU Artificial Intelligence Act (AI Act) and AI-supported hiring, p.36
8 Post-deployment monitoring for discrimination using sensitive data is a necessity - but also a real dilemma. Detecting discrimination in AI systems once they are in use, 41
9 Monitoring with sensitive data lacks a solid legal ground: The GDPR and its exceptions, 46
PART 3: WHAT CAN BE DONE TO COUNTER ALGORITHMIC DISCRIMINATION IN PRACTICE?
10 Detecting and mitigating discrimination in AI hiring systems is possible: Methods to counter discrimination on a technical and socio-technical level, 49
1 Governing AI in hiring is key: Key recommendations, 8
PART 1: WHY IS ALGORITHMIC DISCRIMINATION PROBLEMATIC?
2 Algorithmic discrimination in hiring is a lose-lose scenario that can't be tackled on a technical level only, 14
3 Discrimination can occur at each stage of the AI-assisted hiring process. Sources of bias and other challenges in addressing discrimination in algorithmic hiring, 17
4 Algorithmic discrimination in hiring is real - and significantly affects real people. Experiences of navigating in opaque hiring processes, 22
PART 2: WHAT DOES THE LAW SAY AND WHAT LEGAL LOOPHOLES DOES IT LEAVE?
5 Intersectional discrimination needs to be considered in policy and law. EU Non-discrimination law and its limits, 29
6 Algorithmic discrimination in hiring is difficult to challenge and needs better redress mechanisms. A fragmented landscape of current legal frameworks, 33
7 The AI Act could have done more to tackle algorithmic discrimination in hiring. The EU Artificial Intelligence Act (AI Act) and AI-supported hiring, p.36
8 Post-deployment monitoring for discrimination using sensitive data is a necessity - but also a real dilemma. Detecting discrimination in AI systems once they are in use, 41
9 Monitoring with sensitive data lacks a solid legal ground: The GDPR and its exceptions, 46
PART 3: WHAT CAN BE DONE TO COUNTER ALGORITHMIC DISCRIMINATION IN PRACTICE?
10 Detecting and mitigating discrimination in AI hiring systems is possible: Methods to counter discrimination on a technical and socio-technical level, 49
