Explainable AI in industry
Krishna Gade, Sahin Cem Geyik, Krishnaram Kenthapadi, Varun Mithal, Ankur Taly
Abstract
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.