Article

What Are People Doing About XAI User Experience? A Survey on AI Explainability Research and Practice

Authors:

Juliana J. Ferreira,

Mateus S. MonteiroAuthors Info & Claims

Design, User Experience, and Usability. Design for Contemporary Interactive Environments: 9th International Conference, DUXU 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings, Part II

Pages 56 - 73

https://doi.org/10.1007/978-3-030-49760-6_4

Published: 19 July 2020 Publication History

Abstract

Explainability is a hot topic nowadays for artificial intelligent (AI) systems. The role of machine learning (ML) models on influencing human decisions shed light on the back-box of computing systems. AI based system are more than just ML models. ML models are one element for the AI explainability’ design and needs to be combined with other elements so it can have significant meaning for people using AI systems. There are different goals and motivations for AI explainability. Regardless the goal for AI explainability, there are more to AI explanation than just ML models or algorithms. The explainability of an AI systems behavior needs to consider different dimensions: 1) who is the receiver of that explanation, 2) why that explanation is needed, and 3) in which context and other situated information the explanation is presented. Considering those three dimensions, the explanation can be effective by fitting the user needs and expectation in the right moment and format. The design of an AI explanation user experience is central for the pressing need from people and the society to understand how an AI system may impact on human decisions. In this paper, we present a literature review on AI explainability research and practices. We first looked at the computer science (CS) community research to identify the main research themes about AI explainability, or “explainable AI”. Then, we focus on Human-Computer Interaction (HCI) research trying to answer three questions about the selected publications: to whom the AI explainability is for (who), which is the purpose of the AI explanation (why), and in which context the AI explanation is presented (what + when).

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Cited By

Deters HDroste JHess AKlös VSchneider KSpeith TVogelsang A(2024)The X Factor: On the Relationship between User eXperience and eXplainabilityProceedings of the 13th Nordic Conference on Human-Computer Interaction10.1145/3679318.3685352(1-12)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3679318.3685352
Herbold LSadeghi MVogelsang ADroste JKlös VSchwammberger MSpeith T(2024)Generating Context-Aware Contrastive Explanations in Rule-based SystemsProceedings of the 2024 Workshop on Explainability Engineering10.1145/3648505.3648507(8-14)Online publication date: 20-Apr-2024
https://dl.acm.org/doi/10.1145/3648505.3648507
Schwalbe GFinzel B(2024)A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and conceptsData Mining and Knowledge Discovery10.1007/s10618-022-00867-838:5(3043-3101)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s10618-022-00867-8
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cover image Guide Proceedings

Design, User Experience, and Usability. Design for Contemporary Interactive Environments: 9th International Conference, DUXU 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings, Part II

Jul 2020

743 pages

ISBN:978-3-030-49759-0

DOI:10.1007/978-3-030-49760-6

Editors:
Aaron Marcus
Aaron Marcus and Associates, Berkeley, CA, USA
,
Elizabeth Rosenzweig
World Usability Day and Bentley User Experience Center, Newton Center, MA, USA

© Springer Nature Switzerland AG 2020.

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Springer-Verlag

Berlin, Heidelberg

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Published: 19 July 2020

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Cited By

Deters HDroste JHess AKlös VSchneider KSpeith TVogelsang A(2024)The X Factor: On the Relationship between User eXperience and eXplainabilityProceedings of the 13th Nordic Conference on Human-Computer Interaction10.1145/3679318.3685352(1-12)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3679318.3685352
Herbold LSadeghi MVogelsang ADroste JKlös VSchwammberger MSpeith T(2024)Generating Context-Aware Contrastive Explanations in Rule-based SystemsProceedings of the 2024 Workshop on Explainability Engineering10.1145/3648505.3648507(8-14)Online publication date: 20-Apr-2024
https://dl.acm.org/doi/10.1145/3648505.3648507
Schwalbe GFinzel B(2024)A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and conceptsData Mining and Knowledge Discovery10.1007/s10618-022-00867-838:5(3043-3101)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s10618-022-00867-8
Ziethmann PStieler FPfrommer RSchlögl-Flierl KBauer B(2024)Towards a Framework for Interdisciplinary Studies in Explainable Artificial IntelligenceArtificial Intelligence in HCI10.1007/978-3-031-60606-9_18(316-333)Online publication date: 29-Jun-2024
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Joshi RGraefe JKraus MBengler K(2024)Exploring the Impact of Explainability on Trust and Acceptance of Conversational Agents – A Wizard of Oz StudyArtificial Intelligence in HCI10.1007/978-3-031-60606-9_12(199-218)Online publication date: 29-Jun-2024
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Bertrand AEagan JMaxwell W(2023)Questioning the ability of feature-based explanations to empower non-experts in robo-advised financial decision-makingProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency10.1145/3593013.3594053(943-958)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3593013.3594053
Ibrahim RShafiq M(2023)Explainable Convolutional Neural Networks: A Taxonomy, Review, and Future DirectionsACM Computing Surveys10.1145/356369155:10(1-37)Online publication date: 2-Feb-2023
https://dl.acm.org/doi/10.1145/3563691
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