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Designing Theory-Driven User-Centric Explainable AI

Authors:

Brian Y. LimAuthors Info & Claims

CHI '19: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

Paper No.: 601, Pages 1 - 15

https://doi.org/10.1145/3290605.3300831

Published: 02 May 2019 Publication History

Editorial Notes

A corrigendum was issued for this paper on September 16, 2019. You can download the corrigendum from the source materials section of this citation page.

Abstract

From healthcare to criminal justice, artificial intelligence (AI) is increasingly supporting high-consequence human decisions. This has spurred the field of explainable AI (XAI). This paper seeks to strengthen empirical application-specific investigations of XAI by exploring theoretical underpinnings of human decision making, drawing from the fields of philosophy and psychology. In this paper, we propose a conceptual framework for building human-centered, decision-theory-driven XAI based on an extensive review across these fields. Drawing on this framework, we identify pathways along which human cognitive patterns drives needs for building XAI and how XAI can mitigate common cognitive biases. We then put this framework into practice by designing and implementing an explainable clinical diagnostic tool for intensive care phenotyping and conducting a co-design exercise with clinicians. Thereafter, we draw insights into how this framework bridges algorithm-generated explanations and human decision-making theories. Finally, we discuss implications for XAI design and development.

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Index Terms

Designing Theory-Driven User-Centric Explainable AI
1. Computing methodologies
  1. Artificial intelligence
2. Human-centered computing
  1. Human computer interaction (HCI)

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CHI '19: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

May 2019

9077 pages

ISBN:9781450359702

DOI:10.1145/3290605

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University of Glasgow, Scotland, UK
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University College London, UK
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University of Melbourne, Australia

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Jung ISchuler KZerlik MGrummt SSedlmayr MSedlmayr B(2025)Overview of basic design recommendations for user-centered explanation interfaces for AI-based clinical decision support systems: A scoping reviewDIGITAL HEALTH10.1177/2055207624130829811Online publication date: 23-Jan-2025
https://doi.org/10.1177/20552076241308298
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https://dl.acm.org/doi/10.1145/3706468.3706470
Nguyen HNguyen LCao H(2025)XEdgeAI: A human-centered industrial inspection framework with data-centric Explainable Edge AI approachInformation Fusion10.1016/j.inffus.2024.102782116(102782)Online publication date: Apr-2025
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Zeng JChen KWang RLi YFan MWu KQi XWang L(2025)ContractMind: Trust-calibration interaction design for AI contract review toolsInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2024.103411196(103411)Online publication date: Feb-2025
https://doi.org/10.1016/j.ijhcs.2024.103411
Löfström TLöfström HJohansson USönströd CMatela R(2025)Calibrated explanations for regressionMachine Learning10.1007/s10994-024-06642-8114:4Online publication date: 21-Feb-2025
https://doi.org/10.1007/s10994-024-06642-8
Shonhe LMin Q(2025)Mitigating AI-induced professional identity threat and fostering adoption in the workplaceAI & SOCIETY10.1007/s00146-024-02170-0Online publication date: 15-Jan-2025
https://doi.org/10.1007/s00146-024-02170-0
Cappuccio EFadda DLanzilotti RRinzivillo S(2025)FIPER: A Visual-Based Explanation Combining Rules and Feature ImportanceMachine Learning and Principles and Practice of Knowledge Discovery in Databases10.1007/978-3-031-74633-8_11(171-184)Online publication date: 1-Jan-2025
https://doi.org/10.1007/978-3-031-74633-8_11
Punzi CSetzu MPellungrini RGiannotti FPedreschi D(2025)Towards Synergistic Human-AI Collaboration in Hybrid Decision-Making SystemsMachine Learning and Principles and Practice of Knowledge Discovery in Databases10.1007/978-3-031-74627-7_20(268-275)Online publication date: 1-Jan-2025
https://doi.org/10.1007/978-3-031-74627-7_20
Litvinova YKeplinger K(2025)XAI-Supported Decision-Making: Insights from NeuroIS Studies for a User PerspectiveInformation Systems and Neuroscience10.1007/978-3-031-71385-9_13(157-177)Online publication date: 3-Mar-2025
https://doi.org/10.1007/978-3-031-71385-9_13
Chari SSeneviratne OGhalwash MShirai SGruen DMeyer PChakraborty PMcGuinness D(2024)Explanation Ontology: A general-purpose, semantic representation for supporting user-centered explanationsSemantic Web10.3233/SW-23328215:4(959-989)Online publication date: 4-Oct-2024
https://doi.org/10.3233/SW-233282
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