Diversity and Inclusion in AI for Recruitment: Lessons from Industry Workshop
It addresses the gap in practical implementation of D&I principles in AI recruitment systems, which is crucial for preventing bias and legal risks in hiring, though it appears incremental as it builds on existing guidelines.
This study investigated how to implement diversity and inclusion guidelines in AI-driven recruitment systems through a co-design workshop with a multinational recruitment company, finding that while the workshop increased participants' understanding, translating awareness into practice remained challenging due to conflicts with business goals.
Artificial Intelligence (AI) systems for online recruitment markets have the potential to significantly enhance the efficiency and effectiveness of job placements and even promote fairness or inclusive hiring practices. Neglecting Diversity and Inclusion (D&I) in these systems, however, can perpetuate biases, leading to unfair hiring practices and decreased workplace diversity, while exposing organisations to legal and reputational risks. Despite the acknowledged importance of D&I in AI, there is a gap in research on effectively implementing D&I guidelines in real-world recruitment systems. Challenges include a lack of awareness and framework for operationalising D&I in a cost-effective, context-sensitive manner. This study aims to investigate the practical application of D&I guidelines in AI-driven online job-seeking systems, specifically exploring how these principles can be operationalised to create more inclusive recruitment processes. We conducted a co-design workshop with a large multinational recruitment company focusing on two AI-driven recruitment use cases. User stories and personas were applied to evaluate the impacts of AI on diverse stakeholders. Follow-up interviews were conducted to assess the workshop's long-term effects on participants' awareness and application of D&I principles. The co-design workshop successfully increased participants' understanding of D&I in AI. However, translating awareness into operational practice posed challenges, particularly in balancing D&I with business goals. The results suggest developing tailored D&I guidelines and ongoing support to ensure the effective adoption of inclusive AI practices.