Any AI system that influences decisions about people can produce unequal outcomes across protected groups, even when no group is named in the data. The AI Bias Impact Assessment walks you through the questions regulators and auditors expect you to have answered: who is affected, how harm could occur, what you have done to reduce it, and how you will keep watching.
How it works
You describe the system and the stakes of the decisions it informs, then select which protected characteristics could plausibly be affected. The tool then asks about concrete controls — whether you have measured outcome disparities, tested for proxy variables, built in a human review or appeal path, and committed to ongoing monitoring.
It combines two signals into a residual-risk rating: the decision stakes (a recommendation engine is lower stakes than a hiring or credit decision) and your control coverage (how many mitigation and monitoring measures are in place). High stakes with thin controls produce a high residual risk; the same stakes with disparity testing, an appeal route, and monitoring produce a lower one.
What you get
The output is a structured record: the affected groups, the harm pathways flagged, the controls present and missing, and the residual-risk rating with a short rationale. This maps onto the structure of a UK Equality Impact Assessment and the fundamental-rights impact assessment expected under the EU AI Act for high-risk systems.
Tips and notes
The most commonly missed step is testing for proxy variables — features such as postcode or device type that correlate with a protected characteristic and reintroduce bias you thought you had removed. The second is an appeal or override path, which both reduces harm and is increasingly a regulatory expectation. Treat the assessment as a living document; redo it whenever the model or its context changes. All analysis happens locally in your browser and nothing is uploaded.