AI-Mediated Hiring and the Job Search of Blind and Low-Vision Individuals

2026-01-17

AI Summary

This study addresses the systemic challenges faced by blind and low-vision (BLV) job seekers in AI-driven hiring systems, where misrecognition of identity, dehumanizing interactions, and structural barriers exacerbate employment inequities. Drawing on in-depth interviews with 17 BLV participants and integrating an interdependence theoretical framework with AI literacy analysis, the research reconceptualizes job-seeking as a socially interdependent process rather than an individualistic endeavor. It identifies agentic practices such as “strategic avoidance” and “counter-navigational tactics,” from which three key coping strategies emerge. The work proposes disability-centered design principles for AI hiring tools, advocating for systems that prioritize social connection and collaborative support over algorithmic efficiency alone, thereby offering both theoretical insights and practical guidance for developing more inclusive recruitment technologies.

Technology Category

• Humans and AI: AI for Accessibility
• Philosophy and Ethics of AI: AI & Jobs/Labor
• Computer Vision: Bias, Fairness & Privacy

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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 deployed their own tools to bypass algorithmic screening and built peer networks to share AI literacy. They also practiced'strategic refusal', choosing to avoid certain AI systems to regain their agency. Unlike prior work that frames job search as an individualistic activity, or one focused on being compliant with employer needs, we use the interdependence framework to argue that for BLV people, job search is an interdependent process. We offer design recommendations for AI-mediated tools that center disability perspectives and support interdependencies in job search.

Problem

• AI-mediated hiring
• blind and low-vision
• job search barriers
• algorithmic bias
• disability inclusion

Innovation

• AI-mediated hiring
• blind and low-vision job seekers
• strategic counter-navigation
• interdependence framework
• algorithmic bias