Data-Driven Child Welfare
The discussion highlights the challenges of implementing machine learning in child protective services, emphasizing the importance of community engagement and diverse perspectives. Despite efforts to address fairness, disparities in model performance raise concerns about potential biases in predicting child maltreatment. The conversation also underscores the complexity of translating business problems into machine learning solutions, particularly in specifying target variables effectively.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Legal and Policy Implications of Model Interpretability with Solon Barocas - TWiML Talk #219
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