Optimizing Model Fairness
Sara discusses the importance of interpretability in machine learning models and the balance between fairness implications and model compression. She highlights the need to redistribute weights to mitigate disparate harm and emphasizes the potential for fulfilling multiple constraints in optimization processes.In this clip
From this podcast
Machine Learning Street Talk (MLST)
Sara Hooker - The Hardware Lottery, Sparsity and Fairness
Related Questions