AI-Mediated Hiring and the Job Search of Blind and Low-Vision Individuals
Kashif Imteyaz, Qiushi (Anya) Liang, Yakov Bart, Maitraye Das, Saiph Savage

TL;DR
This study explores how blind and low-vision job seekers experience AI-driven hiring, revealing challenges and strategies they use to navigate systemic barriers, and offers design recommendations to improve inclusivity.
Contribution
The paper provides empirical insights into BLV individuals' experiences with AI hiring systems and proposes inclusive design strategies centered on disability perspectives.
Findings
AI systems misrepresent BLV candidates' identities
BLV job seekers develop counter-navigation strategies
Participants avoid certain AI systems to maintain agency
Abstract
Blind and low-vision (BLV) individuals face high unemployment rates. The job search is becoming harder as more employers use AI-driven systems to screen resumes before a human ever sees them. Such AI systems could inadvertently further disadvantage BLV job seekers, introducing additional barriers to an already difficult process. We lack understanding of BLV job seekers' experiences in today's AI-driven hiring ecosystem. Without such understanding, we risk designing technologies that create new systemic barriers for BLV job seekers rather than providing support. To this end, we conducted interviews with 17 BLV job seekers and analyzed their experiences with AI-powered hiring systems. We found that AI hiring systems misrepresented their professional identities and created dehumanizing interactions. To level the playing field, BLV job seekers used strategic counter-navigation: they…
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