Bias Mitigation

Techniques and strategies used to identify, measure and reduce bias in AI systems throughout the development lifecycle, including pre-processing, in-processing and post-processing methods.

The steps taken to fix unfairness in AI. This could mean cleaning up the training data, adjusting the model or checking the results to make sure no group is being treated unfairly.

Identifying bias is not enough. Your AI risk management process must include proven mitigation strategies at every stage of development and deployment. This is a key area where governance translates directly into reduced organisational risk.