The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI

By D. Shin. Int. J. Hum. Comput. Stud., 2021

1,208 citations recorded by the source corpus.

Abstract Artificial intelligence and algorithmic decision-making processes are increasingly criticized for their black-box nature. Explainable AI approaches to trace human-interpretable decision processes from algorithms have been explored. Yet, little is known about algorithmic explainability from a human factors’ perspective. From the perspective of user interpretability and understandability, this study examines the effect of explainability in AI on user trust and attitudes toward AI. It conceptualizes causability as an antecedent of explainability and as a key cue of an algorithm and examines them in relation to trust by testing how they affect user perceived performance of AI-driven services. The results show the dual roles of causability and explainability in terms of its underlying links to trust and subsequent user behaviors. Explanations of why certain news articles are recommended generate users trust whereas causability of to what extent they can understand the explanations affords users emotional confidence. Causability lends the justification for what and how should be explained as it determines the relative importance of the properties of explainability. The results have implications for the inclusion of causability and explanatory cues in AI systems, which help to increase trust and help users to assess the quality of explanations. Causable explainable AI will help people understand the decision-making process of AI algorithms by bringing transparency and accountability into AI systems.

Subfields: Artificial Intelligence, Health Informatics, Safety Research

View Paper: https://www.semanticscholar.org/paper/2505152ec54d390fa647928a94dec2b7149a9f47

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Citations in the corpus

Expl(AI)n It to Me – Explainable AI and Information Systems Research (2021, neutral): “…types of explanations, subjectively measured intuitiveness and usability of specific interpretability methods, and whether the model interpretability can improve the performance of human decision making, see for example (Doshi-Velez and Kim 2017; Lage et al. 2019; Alufaisan et al. 2020; Shin 2021).”
User Perceptions of Algorithmic Decisions in the Personalized AI System:Perceptual Evaluation of Fairness, Accountability, Transparency, and Explainability (2020, background): “The growth in algorithmic processing in media, however, has been accompanied by serious concerns about discrimination, bias, and systematic skew in decision-making (Shin, 2021).”
Explanations in Autonomous Driving: A Survey (2021, background): “Personalisation is seen to be crucial for the generation of intelligible or understandable explanations [67], [68], [69].”
A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspective (2022, background): “A growing number of researchers argue that fostering and maintaining user trust is the key to calibrating the user-AI relationship (Jacovi et al., 2021; Shin, 2021), achieving trustworthy AI (Smith, 2019), and further unlocking the potential of AI for society (Bughin et al.”
Trust in AI and Its Role in the Acceptance of AI Technologies (2022, background): “For example, Shin (2021d) examined the explainability of algorithms as a key predictor of FAT and tested whether explainability and FAT further predict users’ trust in and perceptions of the functional attributes (i.e., accuracy, personalization, credibility) of algorithmic journalism.”
Trustworthy Artificial Intelligence: A Review (2022, background): “If system users clearly understand the reasons that lead to a particular output and the cases where this system will not work, they tend to trust the decisions more [180].”