“Robotic answers”: Participatory Identification of Risks in AI-Assisted Interview Training by Job Seekers
Abstract
AI-assisted interview hiring and training systems are increasingly positioned as tools to support job seekers; however, their deployment may introduce risks that are not sufficiently understood. Existing research on fairness and accountability in AI systems has largely emphasized model-centric harms, often overlooking how affected users themselves perceive and articulate risks. We present a participatory study that centers job seekers' perspectives on the potential harms of AI-assisted interview training systems. Through a workshop with N=20 job seekers, participants engaged in collective reflection and peer critique of proposed AI-driven feedback designs. An inductive thematic analysis of participants' evaluative feedback reveals four psychological risks: homogenization of communication, overreliance on AI, illusion of human interaction, and reinforcement of stress and anxiety. These risks highlight the experiential and psychological harms that can arise from AI-mediated training. By foregrounding job seekers as active contributors to risk identification, this work contributes a user-centered perspective on AI harms and underscores the importance of participatory approaches for anticipating and mitigating risks in AI-enabled hiring and training systems.
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