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

TL;DR

This paper investigates how blind and low-vision (BLV) job seekers experience AI-mediated hiring, using Bennett's interdependence framework to illuminate relations among people, technologies, and environments in the job search. Through 17 semi-structured interviews, it reveals how AI recruiters can misrepresent BLV identities, trigger dehumanizing interactions, and create new accessibility barriers, while BLV seekers respond with strategic counter-navigation, peer-based AI literacy, and selective technology refusal. The study documents six core findings: strategic use of AI to decode filters, dystopian speed of AI rejection, collaborative learning networks, misrepresentation by AI tools, intentional technology refusal, and patchwork accessibility. The authors propose design implications for interdependent AI hiring systems that reveal dependencies, support collective networks, and safeguard human review pathways, aiming to level the playing field for BLV job seekers. These insights have practical significance for designers, policymakers, and organizations seeking to build more inclusive, transparent, and accountable AI-mediated hiring processes.

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.