Sayak Paul
Machine Learning Street Talk (MLST)
Understanding Machine Learning Interpretability
This chapter explores the complexities of interpretability and explainability in machine learning, contrasting co-evolution approaches with traditional methods. It highlights the responsibilities of engineers in creating ethical and transparent systems and discusses methodologies like data augmentation for model understanding. The speakers also address the challenges of interpreting advanced models, regulatory implications, and advancements like neurosymbolic models and lifelong learning.
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