Promises and Pitfalls of Black-Box Concept Learning Models

• URL: http://arxiv.org/abs/2106.13314v1
• Date: Thu, 24 Jun 2021 21:00:28 GMT
• Title: Promises and Pitfalls of Black-Box Concept Learning Models
• Authors: Anita Mahinpei, Justin Clark, Isaac Lage, Finale Doshi-Velez, Weiwei Pan
• Abstract summary: We show that machine learning models that incorporate concept learning encode information beyond the pre-defined concepts. Natural mitigation strategies do not fully work, rendering the interpretation of the downstream prediction misleading.
• Score: 26.787383014558802
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning models that incorporate concept learning as an intermediate step in their decision making process can match the performance of black-box predictive models while retaining the ability to explain outcomes in human understandable terms. However, we demonstrate that the concept representations learned by these models encode information beyond the pre-defined concepts, and that natural mitigation strategies do not fully work, rendering the interpretation of the downstream prediction misleading. We describe the mechanism underlying the information leakage and suggest recourse for mitigating its effects.

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