Building Trust in Artificial Intelligence, Machine Learning, and Robotics

By Keng Leng Siau, Weiyu Wang. 2018

525 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8662297398bfa04fbff3b473c961928bc01b3970

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians (2019, background): “As AI is rapidly developing, unlike other technologies, there is an absence of a clear definition of the process, functioning, and role of AI [6].”
Seven HCI Grand Challenges (2019, unknown): “In summary, trust is hard to come by, and requires initial trust formation and continuous trust development, not only through transparency, but also through usability, collaboration and communication, data security and privacy, as well as goal congruence (Siau & Wang, 2018).”
The relationship between trust in AI and trustworthy machine learning technologies (2019, background): “The current emphasis on this area reflects recognition that maintaining trust in AI may be critical for ensuring acceptance and successful adoption of AI-driven services and products [81, 67].”
Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy (2019, background): “Trust in AI : There are trust issues with AI (Siau & Wang, 2018).”
The General Attitudes towards Artificial Intelligence Scale (GAAIS): Confirmatory Validation and Associations with Personality, Corporate Distrust, and General Trust (2022, background): “Artificial Intelligence raises issues of trust, and this is a much-researched aspect of AI (see e.g., Araujo et al., 2020; Rheu et al., 2021; Siau & Wang, 2018, for recent examples).”
User Perceptions and Trust of Explainable Machine Learning Fake News Detectors (2022, background): “Within the perspective of humans, users’ ability to understand and use the AI technology can affect their trust in the technology (Hancock et al., 2011; Siau & Wang, 2018).”
Customer experiences in the age of artificial intelligence (2020, unknown): “In the context of AI, recent studies show that trust is key in ensuring the acceptance, continuing progress, and development of this technology (Siau & Wang, 2018).”
The Roles of Personality Traits, AI Anxiety, and Demographic Factors in Attitudes toward Artificial Intelligence (2022, background): “…expectation of harmful future experiences, perceive a greater number of risks, and consequently, feel more anxious and have a less positive attitude toward AI (Chuang et al., 2016; Haqqi & Suzianti, 2020; Park et al., 2022; Schepman & Rodway, 2022; Siau & Wang, 2018; Thanabordeekij et al., 2020).”
Transparency and trust in artificial intelligence systems (2020, background): “First, trust functions as an important prerequisite of technology acceptance, adoption, and use in general (Venkatesh et al., 2016) and for AI in particular (Siau & Wang, 2018).”