Human-Centered Explainable AI (XAI): From Algorithms to User Experiences

• URL: http://arxiv.org/abs/2110.10790v1
• Date: Wed, 20 Oct 2021 21:33:46 GMT
• Title: Human-Centered Explainable AI (XAI): From Algorithms to User Experiences
• Authors: Q. Vera Liao, Kush R. Varshney
• Abstract summary: explainable AI (XAI) has produced a vast collection of algorithms in recent years. The field is starting to embrace inter-disciplinary perspectives and human-centered approaches.
• Score: 29.10123472973571
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As a technical sub-field of artificial intelligence (AI), explainable AI (XAI) has produced a vast collection of algorithms in recent years. However, explainability is an inherently human-centric property and the field is starting to embrace inter-disciplinary perspectives and human-centered approaches. As researchers and practitioners begin to leverage XAI algorithms to build XAI applications, explainability has moved beyond a demand by data scientists or researchers to comprehend the models they are developing, to become an essential requirement for people to trust and adopt AI deployed in numerous domains. Human-computer interaction (HCI) research and user experience (UX) design in this area are therefore increasingly important. In this chapter, we begin with a high-level overview of the technical landscape of XAI algorithms, then selectively survey recent HCI work that takes human-centered approaches to design, evaluate, provide conceptual and methodological tools for XAI. We ask the question "what are human-centered approaches doing for XAI" and highlight three roles that they should play in shaping XAI technologies: to drive technical choices by understanding users' explainability needs, to uncover pitfalls of existing XAI methods through empirical studies and inform new methods, and to provide conceptual frameworks for human-compatible XAI.

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