Example Concept: Developers use techniques such as diverse data sampling, bias detection tools, and fairness-aware algorithms to reduce biases in AI systems. They start by ensuring that the training data is representative of all relevant groups. Then, they apply algorithms that can detect and mitigate biases during model training. Finally, they evaluate the model's performance across different demographic groups to ensure fairness before deployment.