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Are explanations always important?: a study of deployed, low-cost intelligent interactive systems

Authors:

Catherine LauzonAuthors Info & Claims

IUI '12: Proceedings of the 2012 ACM international conference on Intelligent User Interfaces

Pages 169 - 178

https://doi.org/10.1145/2166966.2166996

Published: 14 February 2012 Publication History

Abstract

Intelligent interactive systems (IIS) have great potential to improve users' experience with technology by tailoring their behaviour and appearance to users' individual needs; however, these systems, with their complex algorithms and dynamic behaviour, can also suffer from a lack of comprehensibility and transparency. We present the results of two studies examining the comprehensibility of, and desire for explanations with deployed, low-cost IIS. The first study, a set of interviews with 21 participants, reveals that i) comprehensibility is not always dependent on explanations, and ii) the perceived cost of viewing explanations tends to outweigh the anticipated benefits. Our second study, a two-week diary study with 14 participants, confirms these findings in the context of daily use, with participants indicating a desire for an explanation in only 7% of diary entries. We discuss the implications of our findings for the design of explanation facilities.

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Cited By

Yang NHan EChoi J(2024)Explainability of Image Generative AI for Novice and Expert Users: A Comparative Study of Static and Dynamic ExplanationsJournal of Digital Contents Society10.9728/dcs.2024.25.8.226125:8(2261-2272)Online publication date: 31-Aug-2024
https://doi.org/10.9728/dcs.2024.25.8.2261
Bahel VSriram HConati C(2024)Initial results on personalizing explanations of AI hints in an ITSProceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3627043.3659566(244-248)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3627043.3659566
Anik ABunt A(2024)Supporting User Critiques of AI Systems via Training Dataset Explanations: Investigating Critique Properties and the Impact of Presentation Style2024 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)10.1109/VL/HCC60511.2024.00024(134-147)Online publication date: 2-Sep-2024
https://doi.org/10.1109/VL/HCC60511.2024.00024
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Index Terms

Are explanations always important?: a study of deployed, low-cost intelligent interactive systems
1. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

cover image ACM Conferences

IUI '12: Proceedings of the 2012 ACM international conference on Intelligent User Interfaces

February 2012

436 pages

ISBN:9781450310482

DOI:10.1145/2166966

General Chairs:
Carlos Duarte
University of Lisbon, Portugal
,
Luís Carriço
Universiy of Lisbon, Portugal
,
Program Chairs:
Joaquim Jorge
INESC-ID, Portugal
,
Sharon Oviatt
Incaa Designs, USA
,
Daniel Gonçalves
INESC-ID, Portugal

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 14 February 2012

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IUI '12

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IUI '12: 17th International Conference on Intelligent User Interfaces

February 14 - 17, 2012

Lisbon, Portugal

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Overall Acceptance Rate 746 of 2,811 submissions, 27%

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Cited By

Yang NHan EChoi J(2024)Explainability of Image Generative AI for Novice and Expert Users: A Comparative Study of Static and Dynamic ExplanationsJournal of Digital Contents Society10.9728/dcs.2024.25.8.226125:8(2261-2272)Online publication date: 31-Aug-2024
https://doi.org/10.9728/dcs.2024.25.8.2261
Bahel VSriram HConati C(2024)Initial results on personalizing explanations of AI hints in an ITSProceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3627043.3659566(244-248)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3627043.3659566
Anik ABunt A(2024)Supporting User Critiques of AI Systems via Training Dataset Explanations: Investigating Critique Properties and the Impact of Presentation Style2024 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)10.1109/VL/HCC60511.2024.00024(134-147)Online publication date: 2-Sep-2024
https://doi.org/10.1109/VL/HCC60511.2024.00024
Sadeghi MHerbold LUnterbusch MVogelsang A(2024)SmartEx: A Framework for Generating User-Centric Explanations in Smart Environments2024 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PerCom59722.2024.10494449(106-113)Online publication date: 11-Mar-2024
https://doi.org/10.1109/PerCom59722.2024.10494449
Gambo IMassenon RLin COgundokun RAgarwal SPak W(2024)Enhancing User Trust and Interpretability in AI-Driven Feature Request Detection for Mobile App Reviews: An Explainable ApproachIEEE Access10.1109/ACCESS.2024.344352712(114023-114045)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3443527
Ortigossa EGonçalves TNonato L(2024)EXplainable Artificial Intelligence (XAI)—From Theory to Methods and ApplicationsIEEE Access10.1109/ACCESS.2024.340984312(80799-80846)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3409843
Mill EGarn WRyman-Tubb NTurner C(2024)The SAGE Framework for Explaining Context in Explainable Artificial IntelligenceApplied Artificial Intelligence10.1080/08839514.2024.231867038:1Online publication date: 22-Feb-2024
https://doi.org/10.1080/08839514.2024.2318670
Longo LBrcic MCabitza FChoi JConfalonieri RSer JGuidotti RHayashi YHerrera FHolzinger AJiang RKhosravi HLecue FMalgieri GPáez ASamek WSchneider JSpeith TStumpf S(2024)Explainable Artificial Intelligence (XAI) 2.0Information Fusion10.1016/j.inffus.2024.102301106:COnline publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.inffus.2024.102301
Haque AIslam NMikalef P(2024)To Explain or Not To Explain: An Empirical Investigation of AI-based Recommendations on Social Media PlatformsElectronic Markets10.1007/s12525-024-00741-z35:1Online publication date: 13-Dec-2024
https://doi.org/10.1007/s12525-024-00741-z
Papenkordt J(2024)Navigating Transparency: The Influence of On-demand Explanations on Non-expert User Interaction with AIArtificial Intelligence in HCI10.1007/978-3-031-60606-9_14(238-263)Online publication date: 1-Jun-2024
https://doi.org/10.1007/978-3-031-60606-9_14
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