Biases in ML Part 3B: Risks and Mitigation - Strategies for Fair and Accurate ML Algorithms

This final installation in the Biases in Machine Learning Algorithms series provides actionable strategies for developing, testing, and refining algorithms within Higher Education software to validate the output and predict and mitigate potential harm, ensuring they are both equitable and effective in their application.

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