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.
In plain language
The practical steps taken to reduce unfairness in an AI system. This might mean cleaning up biased data before training, adjusting the model during training or checking and correcting outputs after the model makes predictions.
Why this matters
Identifying bias is not enough; your AI governance framework must specify which mitigation strategies will be applied at each stage of development. This transforms fairness from a compliance checkbox into an operational reality that reduces organisational and user harm.
Relevance
ImplementationBias mitigation involves specific technical choices across the development pipeline and requires governance approval of which methods apply to your systems.
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