Counterfactual, Per-Decision Bias Auditing for Automated Hiring: Localizing and Explaining Disparate Impact in Applicant Tracking Systems

Automated applicant tracking systems (ATS) are widely used in hiring, and recent litigation and regulatory pressures necessitate robust auditing for bias. Current bias assessment methods are limited; group fairness metrics lack individual decision granularity, and local explainers do not align with legal bias standards. A novel method, the AI Bias Firewall (AIBF), is proposed to address these gaps by auditing individual hiring decisions through counterfactual analysis to identify and explain disparate impact.

The increasing reliance on automated systems for critical functions like hiring introduces significant legal and reputational risks if bias is not effectively managed and auditable. Developing robust, legally-aligned methods for bias detection is crucial for maintaining public trust, ensuring fair employment practices, and mitigating potential litigation or regulatory penalties for organizations utilizing such technologies.

Automated applicant tracking systems are increasingly central to candidate progression in hiring processes.

Forward consideration, not a verified fact.