Fair by Design? The Legal and Ethical Challenges of Algorithmic Hiring

This paper critically examines the promise and pitfalls of algorithmic hiring systems through a legal and ethical lens. Focusing on cases such as Pymetrics, HireVue, and Amazon’s résumé screening tool, we explore how automated decision-making in recruitment, despite claims of neutrality and fairness, often reproduces or amplifies existing social inequalities. Drawing from recent legal scholarship and normative theories of algorithmic fairness, we show how systems designed to minimize human bias can inadvertently encode discriminatory assumptions into technical infrastructures. The paper analyzes competing fairness frameworks and emphasizes that fairness is not a purely technical feature, but a normative commitment that must guide every stage of system development and deployment. We argue for a shift from reactive audits to proactive, participatory governance models grounded in transparency, inclusiveness, and accountability. Through the “Developer’s Model for Responsible AI”, we propose a structured, lifecycle-based approach to operationalize fairness in algorithmic systems, especially in sensitive domains like employment. Ultimately, the paper contends that ensuring justice in AI is not only a technical challenge but a democratic imperative.