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Explainable Artificial Intelligence (XAI) from a user perspective: A synthesis of prior literature and problematizing avenues for future research

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  • Haque, AKM Bahalul
  • Islam, A.K.M. Najmul
  • Mikalef, Patrick

Abstract

The rapid growth and use of artificial intelligence (AI)-based systems have raised concerns regarding explainability. Recent studies have discussed the emerging demand for explainable AI (XAI); however, a systematic review of explainable artificial intelligence from an end user's perspective can provide a comprehensive understanding of the current situation and help close the research gap. The purpose of this study was to perform a systematic literature review of explainable AI from the end user's perspective and to synthesize the findings. To be precise, the objectives were to 1) identify the dimensions of end users' explanation needs; 2) investigate the effect of explanation on end user's perceptions, and 3) identify the research gaps and propose future research agendas for XAI, particularly from end users' perspectives based on current knowledge. The final search query for the Systematic Literature Review (SLR) was conducted on July 2022. Initially, we extracted 1707 journal and conference articles from the Scopus and Web of Science databases. Inclusion and exclusion criteria were then applied, and 58 articles were selected for the SLR. The findings show four dimensions that shape the AI explanation, which are format (explanation representation format), completeness (explanation should contain all required information, including the supplementary information), accuracy (information regarding the accuracy of the explanation), and currency (explanation should contain recent information). Moreover, along with the automatic representation of the explanation, the users can request additional information if needed. We have also described five dimensions of XAI effects: trust, transparency, understandability, usability, and fairness. We investigated current knowledge from selected articles to problematize future research agendas as research questions along with possible research paths. Consequently, a comprehensive framework of XAI and its possible effects on user behavior has been developed.

Suggested Citation

  • Haque, AKM Bahalul & Islam, A.K.M. Najmul & Mikalef, Patrick, 2023. "Explainable Artificial Intelligence (XAI) from a user perspective: A synthesis of prior literature and problematizing avenues for future research," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
  • Handle: RePEc:eee:tefoso:v:186:y:2023:i:pa:s0040162522006412
    DOI: 10.1016/j.techfore.2022.122120
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    1. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    2. Barbara H. Wixom & Peter A. Todd, 2005. "A Theoretical Integration of User Satisfaction and Technology Acceptance," Information Systems Research, INFORMS, vol. 16(1), pages 85-102, March.
    3. Fred D. Davis & Richard P. Bagozzi & Paul R. Warshaw, 1989. "User Acceptance of Computer Technology: A Comparison of Two Theoretical Models," Management Science, INFORMS, vol. 35(8), pages 982-1003, August.
    4. Gary C. Moore & Izak Benbasat, 1991. "Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation," Information Systems Research, INFORMS, vol. 2(3), pages 192-222, September.
    5. Antoine Hudon & Théophile Demazure & Alexander Karran & Pierre-Majorique Léger & Sylvain Sénécal, 2021. "Explainable Artificial Intelligence (XAI): How the Visualization of AI Predictions Affects User Cognitive Load and Confidence," Lecture Notes in Information Systems and Organization, in: Fred D. Davis & René Riedl & Jan vom Brocke & Pierre-Majorique Léger & Adriane B. Randolph & Gernot (ed.), Information Systems and Neuroscience, pages 237-246, Springer.
    6. Hengstler, Monika & Enkel, Ellen & Duelli, Selina, 2016. "Applied artificial intelligence and trust—The case of autonomous vehicles and medical assistance devices," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 105-120.
    7. Sarah Bankins & Paul Formosa & Yannick Griep & Deborah Richards, 2022. "AI Decision Making with Dignity? Contrasting Workers’ Justice Perceptions of Human and AI Decision Making in a Human Resource Management Context," Information Systems Frontiers, Springer, vol. 24(3), pages 857-875, June.
    8. Hasan, Rajibul & Shams, Riad & Rahman, Mizan, 2021. "Consumer trust and perceived risk for voice-controlled artificial intelligence: The case of Siri," Journal of Business Research, Elsevier, vol. 131(C), pages 591-597.
    9. Simon Meyer Lauritsen & Mads Kristensen & Mathias Vassard Olsen & Morten Skaarup Larsen & Katrine Meyer Lauritsen & Marianne Johansson Jørgensen & Jeppe Lange & Bo Thiesson, 2020. "Explainable artificial intelligence model to predict acute critical illness from electronic health records," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    10. Gruetzemacher, Ross & Dorner, Florian E. & Bernaola-Alvarez, Niko & Giattino, Charlie & Manheim, David, 2021. "Forecasting AI progress: A research agenda," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    11. Du, Shuili & Xie, Chunyan, 2021. "Paradoxes of artificial intelligence in consumer markets: Ethical challenges and opportunities," Journal of Business Research, Elsevier, vol. 129(C), pages 961-974.
    12. William H. DeLone & Ephraim R. McLean, 1992. "Information Systems Success: The Quest for the Dependent Variable," Information Systems Research, INFORMS, vol. 3(1), pages 60-95, March.
    13. Stahl, B.C. & Andreou, A. & Brey, P. & Hatzakis, T. & Kirichenko, A. & Macnish, K. & Laulhé Shaelou, S. & Patel, A. & Ryan, M. & Wright, D., 2021. "Artificial intelligence for human flourishing – Beyond principles for machine learning," Journal of Business Research, Elsevier, vol. 124(C), pages 374-388.
    14. Mats Alvesson & Jörgen Sandberg, 2020. "The Problematizing Review: A Counterpoint to Elsbach and Van Knippenberg’s Argument for Integrative Reviews," Journal of Management Studies, Wiley Blackwell, vol. 57(6), pages 1290-1304, September.
    15. Mahmud, Hasan & Islam, A.K.M. Najmul & Ahmed, Syed Ishtiaque & Smolander, Kari, 2022. "What influences algorithmic decision-making? A systematic literature review on algorithm aversion," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    16. Davide Castelvecchi, 2016. "Can we open the black box of AI?," Nature, Nature, vol. 538(7623), pages 20-23, October.
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