Explainable AI in industry: practical challenges and lessons learned: implications tutorial

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 [2, 4]. Model explainability is considered a prerequisite for building trust and adoption of AI systems in high stakes domains such as lending and healthcare [1] requiring 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 [5, 6, 8]. In fact, the field of explainability in AI/ML is at an inflexion point. There is a tremendous need from the societal, regulatory, commercial, end-user, and model developer perspectives. Consequently, practical and scalable explainability approaches are rapidly becoming available. The challenges for the research community include: (i) achieving consensus on the right notion of model explainability, (ii) identifying and formalizing explainability tasks from the perspectives of various stakeholders, and (iii) designing measures for evaluating explainability techniques.

In this tutorial, we will first motivate the need for model interpretability and explainability in AI [3] from various perspectives. We will then provide a brief overview of several explainability techniques and tools. The rest of the tutorial will focus on the real-world application of explainability techniques in industry. We will present case studies spanning several domains such as:

• Search and Recommendation systems: Understanding of search and recommendations systems, as well as how retrieval and ranking decisions happen in real-time [7]. Example applications include explanation of decisions made by an AI system towards job recommendations, ranking of potential candidates for job posters, and content recommendations.

• Sales: Understanding of sales predictions in terms of customer up-sell/churn.

• Fraud Detection: Examining and explaining AI systems that determine whether a content or event is fraudulent.

• Lending: How to understand/interpret lending decisions made by an AI system.

We will focus on the sociotechnical dimensions, practical challenges, and lessons learned during development and deployment of these systems, which would be beneficial for researchers and practitioners interested in explainable AI. Finally, we will discuss open challenges and research directions for the community.