Why does AI keep exhibiting bias - and can we fix it?

The answer

AI bias occurs when models systematically produce unfair outcomes, typically reflecting biases embedded in their training data or design choices. A landmark 2018 study by Joy Buolamwini at MIT found commercial facial recognition systems had error rates of up to 34.7% for dark-skinned women versus 0.8% for light-skinned men. Language models trained on internet text absorb and amplify stereotypes about gender, race, and religion. Mitigation techniques include diverse dataset curation, fairness-aware training objectives, bias auditing frameworks like IBM's AI Fairness 360, and rigorous red-teaming. The EU AI Act now mandates bias testing for high-risk AI systems, but experts note that eliminating bias entirely may be impossible since AI fundamentally learns patterns from an inherently biased human world.