Surveilling Suitability: How AI Hiring Interviews Impact Job Seekers with Disabilities

Paper Title

Surveilling Suitability: How AI Hiring Interviews Impact Job Seekers with Disabilities

Publication Info

• Topic area: AI hiring technologies and their impact on job seekers with disabilities
• Keywords: AI hiring interviews, disabilities, surveillance, workplace discrimination, accessibility, normative characteristics, information asymmetry, privacy, autonomy, policy

Background and Problem

• Problem / challenge: AI hiring interviews, designed to streamline hiring and reduce bias, may perpetuate discrimination against people with disabilities by centering normative standards, creating information asymmetries, undermining autonomy, and intruding on privacy.
• Significance: People with disabilities face disproportionately high unemployment rates and systemic barriers to employment. Understanding and addressing the discriminatory effects of AI hiring technologies is critical for equitable workforce participation.
• Motivation and related work: While AI hiring technologies promise efficiency and fairness, prior research has shown that AI can reinforce systemic biases, particularly against marginalized groups. Limited work has explored the specific experiences of people with disabilities in this context, leaving a critical gap that this study addresses.

Solution

• Proposed approach: The study uses a qualitative methodology, including focus groups and interviews with 19 people with disabilities, to investigate their perceptions and experiences with AI hiring interviews.
• Novelty:
  1. Provides rich empirical evidence on how people with disabilities perceive and experience AI hiring interviews.
  2. Frames AI hiring interviews as surveillance infrastructures, highlighting their role in reconfiguring power dynamics and social relations.
  3. Proposes design, policy, and community engagement strategies to mitigate discriminatory effects and foster equitable AI systems.
• Procedure and key techniques:
  • Conducted focus groups (5 sessions, 11 participants) and semi-structured interviews (8 participants).
  • Used thematic analysis to identify four key themes: centering normative characteristics, exacerbating information asymmetries, undermining autonomy, and intruding on privacy.
  • Incorporated an "incremental reveal" section to probe participants’ reactions to AI hiring technologies.

Results

• Concrete findings:
  • AI hiring interviews were perceived as unsettling, alienating, and discriminatory.
  • Participants identified four key issues:
    1. AI enforces normative standards, penalizing non-normative behaviors associated with disabilities.
    2. AI creates information asymmetries, limiting mutuality and transparency in the hiring process.
    3. AI undermines autonomy by constraining disability disclosure and decision-making.
    4. AI intrudes on privacy, particularly through automated disability detection and opaque data practices.
• Advantage over baselines: The study provides a nuanced understanding of how AI hiring technologies affect people with disabilities, a perspective underexplored in prior research.
• Experiments / evaluation:
  • Participants included a diverse group of 19 individuals with various disabilities.
  • Data collection involved focus groups and interviews, with thematic analysis used to identify key barriers and concerns.
• Limitations and future work:
  • Limited to U.S.-based participants and a small sample size.
  • Did not examine specific concerns of individual disability groups.
  • Future work should include larger samples, quantitative studies, and exploration of global contexts.

Summary

This study investigates the discriminatory effects of AI hiring interviews on people with disabilities, identifying four key barriers: centering normative characteristics, exacerbating information asymmetries, undermining autonomy, and intruding on privacy. By framing these technologies as surveillance infrastructures, the research highlights their role in reshaping power dynamics and perpetuating inequities. The findings inform actionable design and policy recommendations, including participatory AI processes, community-led audits, and regulatory protections, to promote equitable hiring practices. This work contributes to the broader understanding of AI’s societal impact on marginalized groups and offers pathways for creating inclusive technologies.