Pith. sign in

REVIEW 2 cited by

Diversity and Inclusion in AI for Recruitment: Lessons from Industry Workshop

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2411.06066 v1 pith:BAUXUTPZ submitted 2024-11-09 cs.AI

classification cs.AI
keywords recruitmentsystemsworkshopawarenessdiversityguidelinesinclusivepractices
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original 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.

Discussion (0). Sign in to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Question Bank to Assess AI Inclusivity: Mapping out the Journey from Diversity Errors to Inclusion Excellence

    cs.AI 2025-06 reject novelty 5.0 of 10

    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.

  2. Diversity and Inclusion in AI: Insights from a Survey of AI/ML Practitioners

    cs.CY 2025-05 conditional novelty 5.0 of 10

    A survey of 61 AI/ML practitioners finds that while most believe diverse teams and data reduce bias, actual practices like bias audits and post-development D&I checks are inconsistent and often missing.

Pith tools