Trust in artificial intelligence: Literature review and main path analysis

Abstract

Artificial Intelligence (AI) is present in various modern systems, but it is still subject to acceptance in many fields. Medical diagnosis, autonomous driving cars, recommender systems and robotics are examples of areas in which some humans distrust AI technology, which ultimately leads to low acceptance rates. Conversely, those same applications can have humans who over rely on AI, acting as recommended by the systems with no criticism regarding the risks of a wrong decision. Therefore, there is an optimal balance with respect to trust in AI, achieved by calibration of expectations and capabilities. In this context, the literature about factors influencing trust in AI and its calibration is scattered among research fields, with no objective summaries of the overall evolution of the theme. In order to close this gap, this paper contributes a literature review of the most influential papers on the subject of trust in AI, selected by quantitative methods. It also proposes a Main Path Analysis of the literature, highlighting how the theme has evolved over the years. As results, researchers will find an overview on trust in AI based on the most important papers objectively selected and also tendencies and opportunities for future research.