Explainable AI (XAI) is the discipline of going deeper within the AI system, identifying the reasoning behind the recommendations, verifying the data, and making the algorithms and the results transparent. XAI attempts to make the analytical recommendations of a system understandable and justifiable — as much as possible. Such explainability reduces biases in AI-based decisions, supports legal compliance, and promotes ethical decisions.
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About The Author
Bhuvan Unhelkar (BE, MDBA, MSc, PhD; FACS) is a Cutter Expert. He has decades of strategic as well as hands-on professional experience in the information and communications technologies (ICT) industry. Dr. Unhelkar is a full Professor at the University of South Florida, Sarasota-Manatee campus. As a founder of MethodScience and PLATiFi, he has demonstrated consulting and training expertise in big data (strategies), business analysis (use cases… Read More
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