Explainable AI in Industry: Practical Challenges and Lessons Learned.

Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with the proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI have become far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability [11]. Model explainability is considered a prerequisite for building trust and adoption of AI systems in high stakes domains such as lending and healthcare [1] which require reliability, safety, and fairness. It is also critical to automated transportation, and other industrial applications with significant socio-economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling. As a consequence, AI researchers and practitioners have focused their attention on explainable AI to help them better trust and understand models at scale [14, 15, 25]. The challenges for the research community include: (i) defining model explainability, (ii) formulating explainability tasks for understanding model behavior and developing solutions for these tasks, and finally (iii) designing measures for evaluating the performance of models in explainability tasks. In this tutorial, we will first motivate the need for model interpretability and explainability in AI [6] from societal, legal, enterprise, end-user, and model developer perspectives, and present techniques & tools for providing explainability as part of AI/ML systems [13]. Then, we will focus on the real-world application of explainability techniques in industry, wherein we present practical challenges & implications for using explainability techniques effectively and 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, hiring, lending, sales, and fraud detection. Finally, based on our experiences in industry, we will identify open problems and research directions for the WWW community.