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The ethical imperative of algorithmic fairness in AI-enabled hiring: a critical analysis of bias, accountability, and justice
Citation. Law, J. (2025). The ethical imperative of algorithmic fairness in AI-enabled hiring: a critical analysis of bias, accountability, and justice. AI and Ethics, 6, 65. https://doi.org/10.1007/s43681-025-00927-x
Summary. This peer-reviewed normative analysis argues that technical fairness interventions and compliance frameworks in AI-enabled hiring may reduce measurable disparities without addressing equitable access to opportunity, recognition, candidate dignity, or procedural fairness. It proposes an ethical-by-design governance model that combines justice, capability, and recognition principles with technical implementation and accountability.
Key claims
- Technical mitigation and regulatory compliance can reduce measurable disparities while leaving structural inequities, constrained opportunity, devaluation of marginalized groups, and procedural unfairness unaddressed.
- A compliance-centered approach can enable “bias washing,” in which formal fairness requirements are met without substantive equity.
- An ethical-by-design governance model should join normative principles to technical implementation so hiring systems distribute opportunity more equitably and uphold candidate dignity.
Evidence & limitations
- This is a critical normative analysis synthesizing prior empirical and legal literature rather than a new empirical study; its claims about intervention limits are interpretive and require empirical evaluation in specific hiring systems.
- The ingested Jina Markdown is a public publisher landing-page extract with the abstract, citation metadata, and references rather than the paywalled full text; Unpaywall reported no open-access location for the DOI on retrieval.