Four practical methods to reduce AI bias in production systems.
First, identify the bias by testing features that shouldn’t have impact, like running loan applications 100 times to see gender differences. Second, help users understand potential bias through UX, like asking specifications when someone requests an image of a nurse. Third, correct the data through prompting, prompt tuning, or fine-tuning with better examples. Fourth, create guardrails or constitutions that let AI critique itself based on rules. Each approach has limitations, and changing underlying biased datasets in large models remains hard.
— Bias in, bias out: Addressing the impact of bias in AI · The Edge