Explainable AI in Industry

Krishna Gade (Fiddler Labs), Sahin Cem Geyik, Krishnaram Kenthapadi, Varun Mithal (LinkedIn), Ankur Taly (Fiddler Labs)

Model explainability is a prerequisite for building trust and adoption of AI systems in high-stakes domains. In this tutorial, we will present an overview of model interpretability and explainability in AI, key regulations/laws, and techniques/tools for providing explainability as part of AI/ML systems. Then, we will focus on the application of explainability techniques in industry, wherein we present the practical challenges/guidelines for using explainability techniques effectively, and the lessons learned from deploying explainable models for several web-scale machine learning and data mining applications. We will present case studies across different companies, spanning application domains such as search and recommendation systems, sales, lending, and fraud detection. Finally, based on our experiences in the industry, we will identify open problems and research directions for the data mining/machine learning community.

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