Chapter 13. Developing Artificial Intelligence (AI) with Equality, Diversity, and Inclusion (EDI): Towards Interdisciplinary Co-Design with Scholars and Practitioners of EDI and AI

In this chapter, we discuss how human biases can be inscribed into the code of the human resource management (HRM) algorithms, embedding and sustaining inequalities while assuming a veneer of objectivity. Critical scholars have provided evidence on algorithmic bias across the field of management. On the back of such evidence, scholars have called for an understanding of the processes through which human resource (HR) algorithms may mask inequality and discrimination, reinforce and replicate social and organisational disparities and, in some instances, even amplify human bias. Our chapter offers practical recommendations on how algorithms and models may be developed to ensure more inclusive and fairer outcomes while stamping out the biases in the data they are trained to examine. Lastly, it discusses the role of equality, diversity, and inclusion (EDI) scholars and practitioners in the new era of artificial intelligence (AI) and offers a toolkit for EDI scholars to work with AI scholars to develop EDI-sensitive AI.