Editorial summary. This is our text summary of an article published by arXiv. Charts, figures, and the author’s full voice are at the original — read it there .
Editorial verdict
Methodologically rigorous academic survey. The balanced multidisciplinary framing is a genuine strength, but the abstract-only submission limits full evaluation — the core contribution of integrating systems, bias measures, and legal aspects across disciplines is credible and fills a documented gap in the literature.
Executive summary
This article presents a multidisciplinary survey of fairness and bias in algorithmic hiring, submitted to arXiv by Alessandro Fabris and revised through June 2025. The problem addressed is the growing adoption of algorithmic tools across recruitment pipelines and the absence of integrated, balanced scholarly treatment of their fairness implications. The authors argue that existing literature is fragmented between two competing narratives: optimistic framings that position algorithms as replacements for biased human recruiters, and pessimistic framings that emphasize the automation of discrimination. The survey synthesizes coverage across six dimensions — systems, biases, measures, mitigation strategies, datasets, and legal aspects — to provide a contextualized account of algorithmic hiring fairness. Key findings include that the fundamental question of whether algorithmic hiring can be less biased than human alternatives remains empirically unresolved. The survey is positioned as a resource for both practitioners and researchers, and concludes by identifying current limitations and opportunities for governance-oriented future research aimed at ensuring equitable outcomes for all stakeholders.
Key insights
- 1The empirical question of whether algorithmic hiring is less biased than human recruitment decisions remains unanswered, undermining trustworthiness claims from both proponents and critics.
- 2Existing research in algorithmic hiring fairness is characterized by partial treatment, with scholars typically aligned to either optimistic or pessimistic narratives rather than integrated analysis.
- 3A multidisciplinary lens — spanning computer science, law, social science, and organizational behavior — is presented as necessary to adequately govern algorithmic hiring systems.
Practical takeaways
- Organizations deploying algorithmic hiring tools operate in a space where comparative bias evidence against human alternatives is not yet established, making independent auditing and ongoing monitoring relevant considerations.
- Legal aspects of algorithmic hiring fairness are treated as integral to technical design decisions, suggesting that compliance and engineering decisions in this domain are not separable.
Source & Provenance
arXiv
Fabris et al.
Not specified
Meta-Analysis/Review
Global
Original source metadata is preserved. AI analysis is generated separately.
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