From the course: AI Data Strategy: Data Procurement and Storage

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Framework for bias mitigation in AI

Framework for bias mitigation in AI

From the course: AI Data Strategy: Data Procurement and Storage

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Framework for bias mitigation in AI

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- [Instructor] Bias manifests differently in traditional ML models compared to generative AI models. While traditional ML models often exhibit disparities in prediction accuracy across different demographic groups, generative AI models can amplify biases in the content they create. Addressing these biases requires a structured framework that accounts for the unique risks of each approach. The framework summarized on the screen here provides a clear way to quantify your bias mitigation efforts and see how you stack up against industry best practices. It directly compares traditional ML and generative AI that's making it easier to understand the unique challenges and solutions for each. Don't worry if it's a lot to process here because I'm going to be breaking these differences down piecemeal. First, there is statistical parity, which measures whether different demographic groups receive similar treatment. These are guidelines rather than fixed rules, but for traditional ML, statistical…

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