How can we ensure AI systems remain unbiased when trained on diverse datasets?

Example Concept: Bias mitigation in AI can be achieved by employing pre-processing, in-processing, and post-processing techniques. Pre-processing involves cleaning and balancing the dataset to ensure diverse representation. In-processing includes using fairness constraints during model training. Post-processing involves adjusting the model's outputs to reduce bias. Tools like fairness dashboards and bias detection algorithms are crucial for monitoring and maintaining fairness across the AI lifecycle.