🤖 AI Summary
Algorithmic hiring, though widely adopted, faces persistent fairness challenges entrenched in the binary narrative of “proxy bias” and “automation bias,” lacking contextualized, interdisciplinary systematic analysis. This study develops the first interdisciplinary framework integrating computer science, social science, law, and policy—unifying algorithmic fairness evaluation (e.g., statistical parity, equal opportunity), bias provenance analysis, explainable AI (XAI), regulatory compliance mapping, and empirical socio-technical research. It systematically synthesizes technical systems, bias origins, evaluation metrics, mitigation strategies, benchmark datasets, and regulatory landscapes. Key contributions include: (1) constructing the first knowledge graph of algorithmic hiring fairness, revealing critical research gaps; and (2) proposing an actionable governance pathway centered on multi-stakeholder collaboration and context-sensitive regulation—enhancing both technical trustworthiness and societal inclusivity of algorithmic hiring systems.
📝 Abstract
Employers are adopting algorithmic hiring technology throughout the recruitment pipeline. Algorithmic fairness is especially applicable in this domain due to its high stakes and structural inequalities. Unfortunately, most work in this space provides partial treatment, often constrained by two competing narratives, optimistically focused on replacing biased recruiter decisions or pessimistically pointing to the automation of discrimination. Whether, and more importantly what types of, algorithmic hiring can be less biased and more beneficial to society than low-tech alternatives currently remains unanswered, to the detriment of trustworthiness. This multidisciplinary survey caters to practitioners and researchers with a balanced and integrated coverage of systems, biases, measures, mitigation strategies, datasets, and legal aspects of algorithmic hiring and fairness. Our work supports a contextualized understanding and governance of this technology by highlighting current opportunities and limitations, providing recommendations for future work to ensure shared benefits for all stakeholders.
