Pitfalls of Explainable ML: An Industry Perspective

• URL: http://arxiv.org/abs/2106.07758v1
• Date: Mon, 14 Jun 2021 21:05:05 GMT
• Title: Pitfalls of Explainable ML: An Industry Perspective
• Authors: Sahil Verma, Aditya Lahiri, John P. Dickerson, Su-In Lee
• Abstract summary: Explanations sit at the core of desirable attributes of a machine learning (ML) system. The goal of explainable ML is to intuitively explain the predictions of a ML system, while adhering to the needs to various stakeholders.
• Score: 29.49574255183219
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As machine learning (ML) systems take a more prominent and central role in contributing to life-impacting decisions, ensuring their trustworthiness and accountability is of utmost importance. Explanations sit at the core of these desirable attributes of a ML system. The emerging field is frequently called ``Explainable AI (XAI)'' or ``Explainable ML.'' The goal of explainable ML is to intuitively explain the predictions of a ML system, while adhering to the needs to various stakeholders. Many explanation techniques were developed with contributions from both academia and industry. However, there are several existing challenges that have not garnered enough interest and serve as roadblocks to widespread adoption of explainable ML. In this short paper, we enumerate challenges in explainable ML from an industry perspective. We hope these challenges will serve as promising future research directions, and would contribute to democratizing explainable ML.

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