Human Resource Management and AI: A Contextual Transparency Database

AI transparency in hiring isn't just a technical problem—it's a social one. The paper argues that understanding how AI systems work in recruiting requires examining the actual professional practices, interactions, and competencies of HR practitioners, not just the technical specifications of the algorithms themselves.

The TARAI Index provides a practical tool for evaluating recruiting AI systems. Rather than abstract transparency frameworks, the researchers created a database that examines concrete factors like product functionality, vendor claims, underlying assumptions, and how clearly the AI's decision-making is explained—offering HR professionals actionable ways to assess these tools.

Transparency isn't a fixed property of AI systems—it changes based on context and who's using it. The paper demonstrates that whether an AI recruiting tool is truly "transparent" depends on the specific workplace practices, the competencies of the people using it, and how it actually functions in real hiring workflows, not just what the vendor claims.

Current AI recruiting systems often operate as "black boxes" that obscure how hiring decisions are made. The research highlights that many AI tools used to mediate access to jobs lack sufficient clarity about their functionality and decision-making processes, raising concerns about fairness in the labor market.

Participatory database design can improve AI accountability in professional settings. By involving HR practitioners in building transparency frameworks iteratively, the researchers show how to create more relevant and usable tools for evaluating AI systems in specific industries and professions.