AI recruitment tool fairness checker
AI hiring tools can screen thousands of candidates in seconds — and replicate historical bias just as fast. Before you procure or deploy one, this checker walks you through the fairness questions that regulators, candidates, and your own legal team will eventually ask: where did the training data come from, was the tool validated, does it pass adverse-impact testing, and can a rejected candidate get an explanation and an appeal?
How it works
You describe the vendor tool and the stage it is used at — screening, interview scoring, or final selection — then answer a structured set of fairness questions grounded in the vendor’s documentation. The checker weights each answer by the tool’s use stage, because a tool that auto-rejects CVs carries far more standalone risk than one that merely suggests questions to a human interviewer. The result is a fairness risk rating plus a punch-list of the unanswered or failing items to put in front of the vendor before you sign.
Tips and notes
- Demand the validation study. A reputable vendor can show that the tool predicts job performance and was tested for adverse impact; vague claims of “AI-powered matching” are a red flag.
- Apply the four-fifths rule. Ask for selection rates by protected group; a group passing below 80% of the top group’s rate signals disparate impact that needs justification.
- Insist on explainability and appeal for any tool that rejects candidates — it is both a fairness safeguard and your legal defence.
- Re-test after every model update. Fairness is not a one-time procurement tick; the vendor changing the model can silently reintroduce bias.