Foundations of Explainable Knowledge-Enabled Systems

• DOI: 10.48550/arxiv.2003.07520
• Publication Date: 2020-01-01
• Authors: Chari, Shruthi; Gruen, Daniel M.; Seneviratne, Oshani; McGuinness, Debor...
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Science - Logic in Computer Science; Artificial Intelligence (cs.AI); Computer Science - Artificial Intelligence; 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; Machine Learning (cs.LG); Logic in Computer Science (cs.LO)

Explainability has been an important goal since the early days of Artificial Intelligence. Several approaches for producing explanations have been developed. However, many of these approaches were tightly coupled with the capabilities of the artificial intelligence systems at the time. With the proliferation of AI-enabled systems in sometimes critical settings, there is a need for them to be explainable to end-users and decision-makers. We present a historical overview of explainable artificial intelligence systems, with a focus on knowledge-enabled systems, spanning the expert systems, cognitive assistants, semantic applications, and machine learning domains. Additionally, borrowing from the strengths of past approaches and identifying gaps needed to make explanations user- and context-focused, we propose new definitions for explanations and explainable knowledge-enabled systems.<br/>S. Chari, D. Gruen, O. Seneviratne, D. L. McGuinness, "Foundations of Explainable Knowledge-Enabled Systems". In: Ilaria Tiddi, Freddy Lecue, Pascal Hitzler (eds.), Knowledge Graphs for eXplainable AI -- Foundations, Applications and Challenges. Studies on the Semantic Web, IOS Press, Amsterdam, 2020, to appear<br/>