A 253-question bank for assessing AI inclusivity, organized into five pillars, built from guidelines, literature, and LLM assistance, but validated only through AI-generated personas.
Diversity and Inclusion in AI for Recruitment: Lessons from Industry Workshop
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
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.
citation-role summary
citation-polarity summary
fields
cs.AI 1years
2025 1verdicts
REJECT 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
A Question Bank to Assess AI Inclusivity: Mapping out the Journey from Diversity Errors to Inclusion Excellence
A 253-question bank for assessing AI inclusivity, organized into five pillars, built from guidelines, literature, and LLM assistance, but validated only through AI-generated personas.