Trust in AI
Progress, challenges, and future directions
Bibliographic Data
| ID | 22235161 |
|---|---|
| Authors | Saleh Afroogh (0009-0004-4883-0844, The University of Texas at Austin, corresponding author), Ali Akbari (0000-0002-5000-5876, Stanford University), Emmie Malone (Lone Star College), Mohammadali Kargar (0000-0002-9911-1856, Texas A&M University), Hananeh Alambeigi (0000-0003-4310-3950, Texas A&M University) |
| Year | 2024 |
| Volume | 11 |
| Issue | 1 |
| Publication date | 2024-11-18 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Humanities and Social Sciences Communications (JOURNAL) |
| Journal identifiers | ISSN: 2662-9992 • E-ISSN: 2662-9992 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1057/s41599-024-04044-8 |
| OpenAlex | W4404470920 |
| Language | EN |
| Citations received | 53 |
| References cited | 226 |
The increasing use of artificial intelligence (AI) systems in our daily lives through various applications, services, and products highlights the significance of trust and distrust in AI from a user perspective. AI-driven systems have significantly diffused into various aspects of our lives, serving as beneficial “tools” used by human agents. These systems are also evolving to act as co-assistants or semi-agents in specific domains, potentially influencing human thought, decision-making, and agency. Trust and distrust in AI serve as regulators and could significantly control the level of this diffusion, as trust can increase, and distrust may reduce the rate of adoption of AI. Recently, a variety of studies focused on the different dimensions of trust and distrust in AI and its relevant considerations. In this systematic literature review, after conceptualizing trust in the current AI literature, we will investigate trust in different types of human–machine interaction and its impact on technology acceptance in different domains. Additionally, we propose a taxonomy of technical (i.e., safety, accuracy, robustness) and non-technical axiological (i.e., ethical, legal, and mixed) trustworthiness metrics, along with some trustworthy measurements. Moreover, we examine major trust-breakers in AI (e.g., autonomy and dignity threats) and trustmakers; and propose some future directions and probable solutions for the transition to a trustworthy AI
Data science · Engineering ethics · Political science · Big Data and Business Intelligence · Computer Science · Engineering · Ethics and Social Impacts of AI · Explainable Artificial Intelligence (XAI · Psychology
Who Accepts AI Labor Displacement? Trust, Social Benefit, and Cross-National Contexts in the Age of Generative AI
Static models for a dynamic world
The impact of AI-powered service on customer continuance usage intention in E-retailing
Advancing sustainable construction
Designing legal interfaces
From Fear of Innovation to AI Dependency
Trusting in the world
Exploring psychological pathways to pedagogical AI overreliance among chinese teachers through cognitive–affective–conative framework
Modeling the factors influencing technology students’ intentions to use AI-driven virtual simulation apps in technical drafting and design education
Reflexivity and positionality statements
A call for transdisciplinary trust research in the artificial intelligence era
Plausibility, persuasion, and truth
Exploring trust in generative AI for higher education institutions
Public trust in AI
EFL teachers’ attitudes toward artificial intelligence in language teaching
Persuasive explanations for recommender systems
The power of seeing less
How positive minds embrace AI
Money matters even for machines
Artificial intelligence in financial institutions
From Mass Media and Social Media to AI
From social trust to AI technology acceptance
The role of anthropomorphized artificial intelligence in shaping consumer mental models and emotional experience in digital branding
Not warm or cold, but appropriate
AI-Powered academic search systems
The influence of artificial intelligence quality on business-to-business relationships on E-commerce platforms
Explain it to me like I’m five
A socio-technical framework for analyzing crop advisors' preferences for AI-based decision support systems
Is intelligent automation a welcome presence in the workplace
Losing the hand on the wheel
Lived, affective, and cultural dimensions of ethical AI
Human–AI Interaction in isolated, confined, and extreme environments
A necessary transition in emotional metaphor of social chatbot technology
Generative AI and LLMs in industry
AI adoption among young Indians
Visual effect analysis of gates at modern industrial heritage sites in China based on eye-tracking technology and the semantic difference method
Exploring Trust and Literacy in Engagement With Generative AI and Science Information Behavior
Spectres of medical AI
Drivers of trust in AI across the domains of finance, law, and healthcare
Reciprocal trust and distrust in artificial intelligence systems
Do emotions matter in AI? The mediating role of emotional response between perceived risk and trust
The use of ChatGPT in the workplace
Beyond accidents and misuse
Artificial Intelligence and Political Trust
Unpacking AI-supported Chinese as a foreign language learning
Artificial Goodwill and Human Vulnerability
Decoding user disclosure intentions in generative AI
Ethics First
Desire without satisfaction
Developing an Efficient Governance Framework for Synthetic Health Data for Canada
Friend or enemy? How perceived present and future influence of AI on self vs. others shape human-AI relations across personality mindsets
Digital Sentiments
"Trust in AI Is a "Fluid Process
Empathy and Moral Development
Transparency and trust in artificial intelligence systems
Toward a Framework for Levels of Robot Autonomy in Human-Robot Interaction
Trustworthy artificial intelligence
Artificial Intelligence, Values, and Alignment
The Ethics of AI Ethics
To Trust or to Think
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Mastering the game of Go with deep neural networks and tree search
Antecedents of Trust and Adoption Intention toward Artificially Intelligent Recommendation Systems in Travel Planning
Trust in Automation
The global landscape of AI ethics guidelines
In AI we trust? Citizen perceptions of AI in government decision making
In AI We Trust
Impacts of Attitudes Toward Government and Corporations on Public Trust in Artificial Intelligence
Philosophical evaluation of the conceptualisation of trust in the NHS’ Code of Conduct for artificial intelligence-driven technology
Limits of trust in medical AI
A Bayesian Trust Inference Model for Human-Multi-Robot Teams
In principle obstacles for empathic AI
In AI we trust? Perceptions about automated decision-making by artificial intelligence
Modeling Trust and Empathy for Socially Interactive Robots
Algorithmic Accountability and Public Reason
Transparency and the Black Box Problem
Empathic Accuracy
Conceptual Bases of Employee Accountability
Privacy versus willingness to disclose in e-commerce exchanges
Trust, transparency, and openness
Computers that care
The mindlessness of ostensibly thoughtful action
Trust and privacy in the context of user-generated health data
Transparency you can trust
| Unique citing works | 53 |
|---|---|
| Citations per year | 53 |
| Citation span | 2025 - 2026 (2) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 50 |