A Review on Human–Machine Trust Evaluation: Human-Centric and Machine-Centric Perspectives

Abstract

As complex autonomous systems become increasingly ubiquitous, their deployment and integration into our daily lives will become a significant endeavor. Human-machine trust relationship is now acknowledged as one of the primary aspects that characterize a successful integration. In the context of human-machine interaction (HMI), proper use of machines and autonomous systems depends both on the human and machine counterparts. On one hand, it depends on how well the human relies on the machine regarding the situation or task at hand based on willingness and experience. On the other hand, it depends on how well the machine carries out the task and how well it conveys important information on how the job is done. Furthermore, proper calibration of trust for effective HMI requires the factors affecting trust to be properly accounted for and their relative importance to be rightly quantified. In this article, the functional understanding of humanmachine trust is viewed from two perspectives-human-centric and machine-centric. The human aspect of the discussion outlines factors, scales, and approaches, which are available to measure and calibrate human trust. The discussion on the machine aspect spans trustworthy artificial intelligence, built-in machine assurances, and ethical frameworks of trustworthy machines.

Using the ISM-ANP-SD combination model to explore the mechanism and intervention strategies of influencing factors of coal mine safety system

“…Over-trust or lack of trust will lead to over-supervision or under-supervision of intelligent systems by miners ( 43 ), making miners react poorly in critical events and leading to safety accidents ( 44 ). Therefore, an appropriate level of trust is essential: operators must understand the capabilities of intelligent systems and adequately monitor them as they approach the limits of their capabilities ( 45 ). Maintaining an appropriate level of trust among miners begins with training miners with a clear and detailed introduction to the functions and operation of the intelligent system, explaining the system's limitations, and improving the miners' level of expertise.…”

Survey of Explainable AI Techniques in Healthcare

“…At present, the academic community is focused on the human-centered development of interpretable technology [ 100 , 101 , 102 ]. Unfortunately, the human–XAI interaction techniques face many challenges that need massive work to be solved [ 103 , 104 ].…”

“…To achieve this goal, many researchers are looking to design more sophisticated interpretation methods to achieve trustfulness [ 99 ]. Currently, the academic community is focused on the human-centered development of interpretable technology [ 100 , 101 , 102 ]. Unfortunately, human–XAI interaction techniques also face many challenges.…”

A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspective

“…Of 23 studies, only seven explicitly define trust, while eight conceptualize it and the remaining nine provide neither. This is likely due to trust being an abstract concept that can be relatively difficult to define or generalize (Gebru et al, 2022;Gulati et al, 2019;Sousa et al, 2016), with dynamic characteristics, meaning that trust can change over time and in different contexts and situations (Elkins & Derrick, 2013). The difficulty in defining trust is reflected by findings that only one of the 23 included studies develop their own trust definition (Yan et al, 2013), whereas six studies use Mayer's and Lee and See's trust definitions (Lee & See, 2004;Mayer et al, 2006).…”