<![CDATA[Artificial intelligence (AI) and sensor-enabled technologies are reshaping recruitment and human resources (HR) management by enabling automated, data-driven candidate evaluation. However, sensor-driven AI systems, such as facial analysis, voice recognition, and biometric monitoring, pose significant ethical and operational risks, particularly the perpetuation of historical biases and opaque decision-making processes. This study investigates these tensions through qualitative analysis of expert interviews with AI developers, HR professionals, and diversity, equity, and inclusion (DEI) strategists, coupled with real-world case examples, including a biodefense firm whose vision-based AI system unintentionally excluded qualified candidates. Findings reveal that while AI-sensor platforms offer efficiency and personalized experiences, they can amplify bias, obscure accountability, and challenge legal compliance if not carefully designed and governed. Participants highlighted urgent needs for algorithmic transparency, human oversight, and inclusive system design to mitigate these risks. In response, this study proposes a human-centered framework for the ethical deployment of AI-sensor technologies in hiring, emphasizing continuous bias auditing, clear governance structures, and regulatory alignment. Ultimately, it argues that the transformative potential of intelligent sensing in HR depends not only on technical sophistication but on embedding these tools within sociotechnical systems committed to fairness, accountability, and inclusion.]]>
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