In AI We Trust? Effects of Agency Locus and Transparency on Uncertainty Reduction in Human–AI Interaction

In AI We Trust? Effects of Agency Locus and Transparency on Uncertainty Reduction in Human–AI Interaction

Machine Learning, Agency Locus, Agency Attribution, Transparency, Uncertainty, Trust

Artificial intelligence (AI) is increasingly used to make decisions for humans. Unlike traditional AI that is programmed to follow human-made rules, machine-learning AI generates rules from data. These machine-generated rules are often unintelligible to humans. Will users feel more uncertainty about decisions governed by such rules? To what extent does rule transparency reduce
uncertainty and increase users’ trust? In a 2  3  2 between-subjects online experiment, 491 participants interacted with a website that was purported to be a decision-making AI system. Three
factors of the AI system were manipulated: agency locus (human-made rules vs. machine-learned rules), transparency (no vs. placebic vs. real explanations), and task (detecting fake news vs.
assessing personality). Results show that machine-learning AI triggered less social presence, which
increased uncertainty and lowered trust. Transparency reduced uncertainty and enhanced trust, but the mechanisms for this effect differed between the two types of AI.

Bingjie Liu

https://academic.oup.com/jcmc/article/26/6/384/6367958

Oxford University Press

9 September 2021

Sri Wahyuni

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English

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Journal of Computer-Mediated Communication 26 (2021)