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.

翻译：盲人及低视力（BLV）人群面临较高的失业率。随着越来越多的雇主在人工筛选前使用人工智能系统进行简历初筛，求职过程正变得愈发困难。此类人工智能系统可能无意中进一步加剧BLV求职者的劣势，为本就艰难的求职过程增设额外障碍。目前我们对于BLV求职者在当今人工智能驱动的招聘生态系统中的经历缺乏理解。缺乏这种认知将导致我们设计的技术可能为BLV求职者制造新的系统性障碍，而非提供支持。为此，我们对17名BLV求职者进行了访谈，并分析了他们与人工智能招聘系统的交互经历。研究发现，人工智能招聘系统扭曲了他们的职业身份认同，并制造了去人性化的互动体验。为创造公平竞争环境，BLV求职者采取了战略性逆向导航策略：他们运用自有工具绕过算法筛选，并构建同伴网络以共享人工智能素养知识。同时他们践行"战略性拒绝"，通过主动避开特定人工智能系统以重获自主权。与既往将求职视为个体化活动或强调顺应雇主需求的研究不同，本研究采用相互依存理论框架，论证了对于BLV人群而言，求职本质上是相互依存的过程。最后，我们提出了以残障视角为核心、支持求职过程中相互依存关系的人工智能中介工具设计建议。