International Journal of Human–Computer Interaction

Trust and Mistrust in Artificial Intelligence: Human, Technological and Societal Impact Considerations

Introduction

Trust is a central concept across scholarly and public discussions in politics, economics, and society. The literature offers numerous definitions, extensive investigations into its antecedents, and even lines of research seeking to understand its neurological foundations. Achieving a comprehensive understanding of trust requires attention to its many dimensions: the dispositions, perceptions, beliefs, attitudes, expectations, and intentions of the trustor; the qualities and behaviors of the trustee; and the contextual conditions shaping their interaction. Its significance in human relationships is profound, serving as a foundational element that supports the cohesion of societies, underpinning every form of social interaction, evolving over time, shaped through observation and learning.

As technological systems become more deeply integrated into everyday life, trust in technology has gained equal prominence. Existing literature distinguishes between two main approaches to conceptualizing trust in technology. One direction adapts human-oriented dimensions such as benevolence, integrity, and ability, while the other emphasizes system-oriented attributes such as helpfulness, reliability, and functionality. When it comes to traditional technological artifacts, users rarely view them as moral agents.

The rise of Artificial Intelligence (AI), however, has introduced new complexity into how trust in technology is understood. AI systems now undertake tasks that were once exclusively human, such as offering recommendations that influence personal and institutional decisions, automating processes, engaging in creative tasks, such as writing texts and creating images, interacting with users in ways that can appear distinctly human-like. Yet despite these capacities, AI is fundamentally distinct from humans. It remains a non-conscious digital operating system, with correspondingly different cognitive qualities than biological creatures. Research demonstrates that trust in AI diverges markedly from interpersonal trust across multiple dimensions, including its underlying bases, the way it must be calibrated across contexts, the qualities attributed to the AI system, and the persistent paradox in which individuals extend trust to algorithms despite knowing they can make errors, mislead, or “hallucinate.”

Scope

This Special Issue is dedicated to exploring trust and mistrust in artificial intelligence. It seeks original, rigorous, and impactful contributions that address relevant foundations, challenges, as well as technological and human considerations. Relevant topics may include, but are not limited to:

• Foundational theories and conceptual frameworks of trust and mistrust
• Psychological, cognitive, and behavioral determinants of trusting or mistrusting AI
• Behavioral investigations of trust calibration, over-trust, and under-trust in AI
• Comparative examinations of trust in humans, traditional technologies, and AI systems
• User perceptions, expectations, and mental models of AI trustworthiness
• Cross-cultural, demographic, or contextual variations in AI trust and mistrust
• Design and evaluation of trust-enabling, trust-repair, or trust-calibration strategies
• Metrics, instruments, and modelling approaches for assessing trust in AI or AI trustworthiness
• Technical methods for engineering AI reliability, transparency, robustness, and accountability
• Misinformation, disinformation, deepfakes, and synthetic media
• Bias mitigation and fairness-enhancing algorithms
• Privacy-preserving machine learning
• Data misuse, surveillance, and the impact on trust in AI systems
• Human–AI interaction and collaboration approaches that influence trust dynamics
• Human-Centered Design of trustworthy AI
• Ethical, legal, and policy considerations related to trustworthy and responsible AI
• Governance models, regulatory mechanisms, and oversight structures shaping trust and mistrust in AI
• Domain-specific investigations of trust in areas such as healthcare, education, transportation, public administration, creative industries, robotics, or defence
• Explorations of mistrust, scepticism, resistance, and contestation of AI systems
• Interdisciplinary perspectives on the AI trust paradox and its implications

We encourage work that identifies emerging challenges, proposes innovative solutions, or develops frameworks that can guide future research and practice. Contributions should offer strong scientific grounding, methodological rigor, and clear relevance to the technological and societal dimensions of trust in AI. Interdisciplinary approaches are especially welcome.