AI Bias.

AI bias can arise from biased training data, flawed data collection, or algorithm design. Examples include facial recognition performing poorly on darker skin, and hiring algorithms discriminating against women. Addressing bias requires diverse teams, careful data curation, bias testing, and ongoing monitoring. It's a major challenge for responsible AI.