AI-driven active sourcing in recruitment: addressing contestability in automated hiring systems - Hadžic´, Bakir; Brandner, Lou Therese; Weber, Thomas; Rätsch, Matthias (Frontiers in Computer Science, 2025)
AI-based recruiting is emerging as a critical tool for companies to attract and engage candidates. In this interdisciplinary study, we present a framework for AI-based active sourcing in recruitment to explore opportunities for incorporating considerations of contestability, i.e., the openness of an AI system to human intervention by those affected. The proposed framework is structured around four key modules: Active searching, skills extraction, skills matching, and automated and personalized approach. After introducing the design and functionality of each module, we critically examine the associated opportunities and challenges regarding contestability, including their connection to other ethical aspects like transparency. We conclude with a discussion of pertinent challenges and points of concern, as well as potential practical solutions to enhance contestability and mitigate ethical risks. Our work aims to explore contestability in the context of responsible, ethically acceptable development and application of AI-driven active sourcing systems in human resource management. Future research should empirically assess the integration of contestability aspects in active sourcing approaches in practice.