539: Interpretable Machine Learning — with Serg Masís
Super Data Science: ML & AI Podcast with Jon Krohn
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The Future of Interpretable Machine Learning and Causal Inference
The chapter explores the potential convergence of interpretable machine learning with causal inference, emphasizing the importance of a legal and technical framework. It envisions a future where machine learning is more accessible through drag-and-drop tools, allowing for easier testing of hypotheses and increased trust in results. The discussion also touches upon topics like AutoML, fairness, accountability, transparency, and the upcoming second edition of a book with a dedicated chapter on NLP Transformers.
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Transcript
