Explainable AI

Current research on explainability and interpretability of machine learning algorithms

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Pitfalls of Explainable ML: An Industry Perspective

Sahil Verma, Aditya Lahiri, John P. Dickerson, Su-In Lee. 2021

[ArXiv]    

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)'' orExplainable 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.