Bias and Fairness in AI: The Honest Picture

AI bias is real, measurable, and not solvable by good intentions. It enters through training data, labeling, and optimization choices, and it manifests in subtle, sometimes legally-significant ways.

What AI does well here

• Measuring outcome differences across demographic groups
• Auditing training data for representation gaps
• Using counterfactual prompts to surface bias in outputs
• Documenting known limits in model cards and product docs

What AI cannot do

• Eliminate bias by debiasing prompts alone
• Fix data-set bias post-hoc cleanly
• Substitute for diverse evaluation teams and stakeholder review