Artificial Intelligence Decision-Making Transparency and Employees’ Trust: The Parallel Multiple Mediating Effect of Effectiveness and Discomfort

Abstract

The purpose of this paper is to investigate how Artificial Intelligence (AI) decision-making transparency affects humans’ trust in AI. Previous studies have shown inconsistent conclusions about the relationship between AI transparency and humans’ trust in AI (i.e., a positive correlation, non-correlation, or an inverted U-shaped relationship). Based on the stimulus-organism-response (SOR) model, algorithmic reductionism, and social identity theory, this paper explores the impact of AI decision-making transparency on humans’ trust in AI from cognitive and emotional perspectives. A total of 235 participants with previous work experience were recruited online to complete the experimental vignette. The results showed that employees’ perceived transparency, employees’ perceived effectiveness of AI, and employees’ discomfort with AI played mediating roles in the relationship between AI decision-making transparency and employees’ trust in AI. Specifically, AI decision-making transparency (vs. non-transparency) led to higher perceived transparency, which in turn increased both effectiveness (which promoted trust) and discomfort (which inhibited trust). This parallel multiple mediating effect can partly explain the inconsistent findings in previous studies on the relationship between AI transparency and humans’ trust in AI. This research has practical significance because it puts forward suggestions for enterprises to improve employees’ trust in AI, so that employees can better collaborate with AI.

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“…Furthermore, Trust in AI can also be influenced by personal, organizational, and policy factors [7], as well as properties of the AI system, including controllability, model complexity, embedded biases, and reliability [11]. Transparency is often viewed as a prerequisite for trust in society, and the positive correlation between AI system transparency and trust has been confirmed by previous empirical studies [12,13].…”

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“…If the transparency is not good, the users will be reluctant to accept it. Trust also has the same effect on acceptance [72]. AI must also benefit those with special needs.…”

“…The goals of AI research and development should be based on the principles that it should be economical, according to the law, and ethical [71]. Factors such as transparency [72], safety [73], privacy [74], human control [75], etc., must be considered while working on the development or adoption of AI. In the education sector, in particular, ethical issues related to privacy, safety, transparency, cost, trust, user-friendliness, etc., must be considered [64].…”

“…AI is also impacting the mental health of students and teachers. When they use AI technology, their power to make decisions decreases, and they become more dependent on technology [72]. This dependency further increases the dependency on AI technology and decreases the use of mental power [78].…”

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“…Moreover, transparency stands out as an essential component that affects trust and decision-making in AI systems, according to the research by Yu and Li (2022). Not only does the transparency of AI algorithms and processes determine the levels of employee trust, but it also defines the effectiveness and comfort levels associated with AI-driven technologies.…”