Book contents
- Human-Centered Machine Learning
- Reviews
- Human-Centered Machine Learning
- Copyright page
- Contents
- Contributors
- 1 Introduction
- Part I Human-Centered Machine Learning in the Arts and Humanities
- Part II Doing Human-Centered Machine Learning
- Part III Humans and Machine Learning
- 8 Human-Centered Explainable AI (XAI)
- 9 Humans Teaching Machine Learning Agents
- 10 Modeling the Social and Normative Context of Human–Agent Interactions
- 11 Interviews: Teams
- Part IV Machine Learning in Its Social Context
- Part V Machine Learning and Humanity
- References
8 - Human-Centered Explainable AI (XAI)
From Algorithms to User Experiences
from Part III - Humans and Machine Learning
Published online by Cambridge University Press: 11 September 2026
Book contents
- Human-Centered Machine Learning
- Reviews
- Human-Centered Machine Learning
- Copyright page
- Contents
- Contributors
- 1 Introduction
- Part I Human-Centered Machine Learning in the Arts and Humanities
- Part II Doing Human-Centered Machine Learning
- Part III Humans and Machine Learning
- 8 Human-Centered Explainable AI (XAI)
- 9 Humans Teaching Machine Learning Agents
- 10 Modeling the Social and Normative Context of Human–Agent Interactions
- 11 Interviews: Teams
- Part IV Machine Learning in Its Social Context
- Part V Machine Learning and Humanity
- References
Summary
In recent years, the field of explainable AI (XAI) has produced a vast collection of algorithms, providing a useful toolbox for researchers and practitioners to build XAI applications. With the rich application opportunities, explainability is believed to have moved beyond a demand by data scientists or researchers to comprehend the models they develop, to an essential requirement for people to trust and adopt AI deployed in numerous domains. However, explainability is an inherently human-centric property and the field is starting to embrace human-centered approaches. Human–computer interaction (HCI) research and user experience (UX) design in this area are becoming increasingly important. In this chapter, we begin with a high-level overview of the technical landscape of XAI algorithms, then selectively survey our own and other recent HCI works that take human-centered approaches to design, evaluate, and provide conceptual and methodological tools for XAI. We ask the question “what are human-centered approaches doing for XAI” and highlight three roles they play in shaping XAI technologies by helping navigate, assess, and expand the XAI toolbox: to drive technical choices by users’ explainability needs, to uncover pitfalls of existing XAI methods and inform new methods, and to provide conceptual frameworks for human-compatible XAI.
Keywords
Information
- Type
- Chapter
- Information
- Human-Centered Machine Learning , pp. 173 - 200Publisher: Cambridge University PressPrint publication year: 2026
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