Engineer's Checklist for Auditing ML-Based Hiring Tools Before You Sign

A technical guide aimed at engineers tasked with evaluating ML-based hiring tools warns that vendor demos are designed to obscure critical flaws in model design, data pipelines, and system integration. Engineers are advised to probe whether per-decision explanations are genuinely model-faithful or merely cosmetic UI additions bolted on after the fact. The checklist highlights that models trained on historical hiring data risk replicating past biases, and that claims of being 'bias-free by design' are a red flag from any serious ML practitioner. Integration failures are identified as the leading reason recruiting tools end up unused, with 'API available' falling far short of production-ready compatibility. The guide recommends running a 30-day parallel pilot using your own candidates and roles, tracking metrics like override rate and pass-rate stability across groups, and securing key commitments in writing before any contract is signed.