IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v188y2023ics0040162523000288.html
   My bibliography  Save this article

How should the results of artificial intelligence be explained to users? - Research on consumer preferences in user-centered explainable artificial intelligence

Author

Listed:
  • Kim, Doha
  • Song, Yeosol
  • Kim, Songyie
  • Lee, Sewang
  • Wu, Yanqin
  • Shin, Jungwoo
  • Lee, Daeho

Abstract

Artificial intelligence (AI) has become part of our everyday lives, and its presence and influence are expected to grow exponentially. Regardless of its expanding impact, the perplexing algorithms and processes that drive AI's decision and output can lead to decreased trust, and thus impede the adoption of future AI services. Explainable AI (XAI) in recommender systems has surfaced as a solution that can help users understand how and why an AI recommended a specific product or service. However, there is no standardized explanation method that satisfies users' preferences and needs. Therefore, the main objective of this study is to explore a unified explanation method that centers around human perspective. This study examines the preference for AI interfaces by investigating the components of user-centered explainability, including scope (global and local) and format (text and visualization). A mixed logit model is used to analyze data collected by a conjoint survey. Results show that local explanation and visualization are preferred, and users dislike lengthy textual interfaces. Our findings incorporate the extraction of monetary value from each attribute.

Suggested Citation

  • Kim, Doha & Song, Yeosol & Kim, Songyie & Lee, Sewang & Wu, Yanqin & Shin, Jungwoo & Lee, Daeho, 2023. "How should the results of artificial intelligence be explained to users? - Research on consumer preferences in user-centered explainable artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    • Handle: RePEc:eee:tefoso:v:188:y:2023:i:c:s0040162523000288
    DOI: 10.1016/j.techfore.2023.122343
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162523000288
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2023.122343?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Joel Huber and Kenneth Train., 2000. "On the Similarity of Classical and Bayesian Estimates of Individual Mean Partworths," Economics Working Papers E00-289, University of California at Berkeley.
    2. Frank Ebbers & Jan Zibuschka & Christian Zimmermann & Oliver Hinz, 2021. "User preferences for privacy features in digital assistants," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 411-426, June.
    3. Arun Rai, 2020. "Explainable AI: from black box to glass box," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 137-141, January.
    4. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, November.
    5. Gryz, Jarek & Rojszczak, Marcin, 2021. "Black box algorithms and the rights of individuals: No easy solution to the "explainability" problem," Internet Policy Review: Journal on Internet Regulation, Alexander von Humboldt Institute for Internet and Society (HIIG), Berlin, vol. 10(2), pages 1-24.
    6. Ko, Sungmin & Shin, Jungwoo, 2023. "Projection of fuel cell electric vehicle demand reflecting the feedback effects between market conditions and market share affected by spatial factors," Energy Policy, Elsevier, vol. 173(C).
    7. Ryan, Mandy, 1999. "Using conjoint analysis to take account of patient preferences and go beyond health outcomes: an application to in vitro fertilisation," Social Science & Medicine, Elsevier, vol. 48(4), pages 535-546, February.
    8. Woo, JongRoul & Shin, Jungwoo & Kim, Hongbum & Moon, HyungBin, 2022. "Which consumers are willing to pay for smart car healthcare services? A discrete choice experiment approach," Journal of Retailing and Consumer Services, Elsevier, vol. 69(C).
    9. Green, Paul E & Srinivasan, V, 1978. "Conjoint Analysis in Consumer Research: Issues and Outlook," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 5(2), pages 103-123, Se.
    10. Shin, Jungwoo & Park, Yuri & Lee, Daeho, 2016. "Strategic management of over-the-top services: Focusing on Korean consumer adoption behavior," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 329-337.
    11. Allenby, Greg M. & Rossi, Peter E., 1998. "Marketing models of consumer heterogeneity," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 57-78, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kim, Woojae & Cho, Youngsang, 2024. "Analysis of consumer preferences for new electric vehicle technologies: Can future vehicle steering system steer consumer's purchase intention?," Technology in Society, Elsevier, vol. 78(C).
    2. Xu, Qianwen Ariel & Jayne, Chrisina & Chang, Victor, 2024. "An emoji feature-incorporated multi-view deep learning for explainable sentiment classification of social media reviews," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    3. Shajalal, Md & Boden, Alexander & Stevens, Gunnar, 2024. "ForecastExplainer: Explainable household energy demand forecasting by approximating shapley values using DeepLIFT," Technological Forecasting and Social Change, Elsevier, vol. 206(C).
    4. Ghosh, Indranil & Alfaro-Cortés, Esteban & Gámez, Matías & García-Rubio, Noelia, 2024. "Reflections of public perception of Russia-Ukraine conflict and Metaverse on the financial outlook of Metaverse coins: Fresh evidence from Reddit sentiment analysis," International Review of Financial Analysis, Elsevier, vol. 93(C).
    5. Hülter, Svenja M. & Ertel, Christian & Heidemann, Ansgar, 2024. "Exploring the individual adoption of human resource analytics: Behavioural beliefs and the role of machine learning characteristics," Technological Forecasting and Social Change, Elsevier, vol. 208(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kyuho Maeng & Sungmin Ko & Jungwoo Shin & Youngsang Cho, 2020. "How Much Electricity Sharing Will Electric Vehicle Owners Allow from Their Battery? Incorporating Vehicle-to-Grid Technology and Electricity Generation Mix," Energies, MDPI, vol. 13(16), pages 1-25, August.
    2. Park, Soyeong & Maeng, Kyuho & Shin, Jungwoo, 2023. "Efficient subsidy distribution for hydrogen fuel cell vehicles based on demand segmentation," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    3. Ahn, Jiwoon & Jeong, Gicheol & Kim, Yeonbae, 2008. "A forecast of household ownership and use of alternative fuel vehicles: A multiple discrete-continuous choice approach," Energy Economics, Elsevier, vol. 30(5), pages 2091-2104, September.
    4. Shin, Jungwoo & Park, Yuri & Lee, Daeho, 2016. "Strategic management of over-the-top services: Focusing on Korean consumer adoption behavior," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 329-337.
    5. Jungwoo Shin & Taehoon Lim & Moo Yeon Kim & Jae Young Choi, 2018. "Can Next-Generation Vehicles Sustainably Survive in the Automobile Market? Evidence from Ex-Ante Market Simulation and Segmentation," Sustainability, MDPI, vol. 10(3), pages 1-16, February.
    6. Mueller, Milton L. & Park, Yuri & Lee, Jongsu & Kim, Tai-Yoo, 2006. "Digital identity: How users value the attributes of online identifiers," Information Economics and Policy, Elsevier, vol. 18(4), pages 405-422, November.
    7. Daeho Lee & Jungwoo Shin & Junseok Hwang, 2011. "Application-Based Quality Assessment of Internet Access Service," TEMEP Discussion Papers 201183, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Nov 2011.
    8. Kim, Junghun & Seung, Hyunchan & Lee, Jongsu & Ahn, Joongha, 2020. "Asymmetric preference and loss aversion for electric vehicles: The reference-dependent choice model capturing different preference directions," Energy Economics, Elsevier, vol. 86(C).
    9. Byun, Hyunsuk & Shin, Jungwoo & Lee, Chul-Yong, 2018. "Using a discrete choice experiment to predict the penetration possibility of environmentally friendly vehicles," Energy, Elsevier, vol. 144(C), pages 312-321.
    10. Shin, Jungwoo & Park, Yuri & Lee, Daeho, 2018. "Who will be smart home users? An analysis of adoption and diffusion of smart homes," Technological Forecasting and Social Change, Elsevier, vol. 134(C), pages 246-253.
    11. Lee, Daeho & Shin, Jungwoo & Lee, Sangwon, 2015. "Network management in the era of convergence: Focusing on application-based quality assessment of Internet access service," Telecommunications Policy, Elsevier, vol. 39(8), pages 705-716.
    12. repec:ebl:ecbull:v:30:y:2010:i:1:p:437-449 is not listed on IDEAS
    13. Ho Seoung Na & Junseok Hwang & Hongbum Kim, 2023. "Which Attributes Should be Considered in Regulating the Internet of Things? Evidence From Conjoint Analysis," SAGE Open, , vol. 13(4), pages 21582440231, November.
    14. Sung-Yoon Huh & JongRoul Woo & Chul-Yong Lee, 2019. "What Do Potential Residents Really Want When Hosting a Nuclear Power Plant? An Empirical Study of Economic Incentives in South Korea," Energies, MDPI, vol. 12(7), pages 1-17, March.
    15. Yuri Park & Hyunnam Kim & Jongsu Lee, 2009. "Model for Studying Commodity Bundling with a Focus on Consumer Preference," TEMEP Discussion Papers 200935, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Nov 2009.
    16. Kim, Yeonbae, 2005. "Estimation of consumer preferences on new telecommunications services: IMT-2000 service in Korea," Information Economics and Policy, Elsevier, vol. 17(1), pages 73-84, January.
    17. Mikołaj Czajkowski & Marek Giergiczny & Jakub Kronenberg & Jeffrey Englin, 2019. "The Individual Travel Cost Method with Consumer-Specific Values of Travel Time Savings," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 74(3), pages 961-984, November.
    18. Jung-Kyu Jung & Jae Young Choi, 2022. "Choice and allocation characteristics of faculty time in Korea: effects of tenure, research performance, and external shock," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2847-2869, May.
    19. Regier, Dean A. & Ryan, Mandy & Phimister, Euan & Marra, Carlo A., 2009. "Bayesian and classical estimation of mixed logit: An application to genetic testing," Journal of Health Economics, Elsevier, vol. 28(3), pages 598-610, May.
    20. Woo, JongRoul & Choi, Jae Young & Shin, Jungwoo & Lee, Jongsu, 2014. "The effect of new media on consumer media usage: An empirical study in South Korea," Technological Forecasting and Social Change, Elsevier, vol. 89(C), pages 3-11.
    21. Kim, Sung Hoo & Mokhtarian, Patricia L., 2023. "Finite mixture (or latent class) modeling in transportation: Trends, usage, potential, and future directions," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 134-173.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:tefoso:v:188:y:2023:i:c:s0040162523000288. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.