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What Makes a Good Explanation?: A Unified View of Properties of Interpretable ML
Varshini Subhash · Zixi Chen · Marton Havasi · Weiwei Pan · Finale Doshi-Velez

Fri Dec 02 12:53 PM -- 12:59 PM (PST) @
Event URL: https://openreview.net/forum?id=YDyLZWwpBK2 »

Interpretability provides a means for humans to verify aspects of machine learning (ML) models. Different tasks require explanations with different properties. However, presently, there is a lack of standardization in assessing properties of explanations: different papers use the same term to mean different quantities, and different terms to mean the same quantity. This lack of standardization prevents us from rigorously comparing explanation systems. In this work, we survey explanation properties defined in the current interpretable ML literature, we synthesize properties based on what they measure, and describe the trade-offs between different formulations of these properties. We provide a unifying framework for comparing properties of interpretable ML.

Author Information

Varshini Subhash (Harvard University)
Zixi Chen (Harvard University)
Marton Havasi (Harvard SEAS)
Weiwei Pan (Harvard University)
Finale Doshi-Velez (Harvard)

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