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stat.ML2021★ 7 cited
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations
Neil Jethani, Mukund Sudarshan, Yindalon Aphinyanaphongs +1
While the need for interpretable machine learning has been established, many common approaches are slow, lack fidelity, or hard to evaluate. Amortized explanation methods reduce th…
stat.ML2017
A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations
Josua Krause, Aritra Dasgupta, Jordan Swartz +2
Human-in-the-loop data analysis applications necessitate greater transparency in machine learning models for experts to understand and trust their decisions. To this end, we propos…