communicating trust in AI

Introduction

Trust in artificial intelligence (AI) is a critical factor influencing the acceptance and effective use of AI technologies. As AI becomes increasingly integrated into various aspects of daily life, understanding how to communicate and foster trust in these systems is essential for their successful adoption.

Key Insights

Role of Trust in AI Adoption:
- Trust significantly impacts the intention to use AI technologies, mediated by perceived usefulness and user attitudes .
- Trust in AI is a multi-dimensional construct, including human-like trust and functionality trust, with functionality trust having a greater impact on usage intention.
Factors Influencing Trust:
- Trust in AI is influenced by user characteristics (e.g., age, culture, personality), environmental factors (e.g., task difficulty, perceived risks), and learned factors (e.g., AI reliability, consistency).
- Design factors such as transparency, ease of use, and anthropomorphism (making AI appear human-like) can enhance trust .
Communicating AI Performance:
- The way AI performance information is communicated affects trust. Providing no performance information can sometimes result in higher trust compared to providing negative or positive information.
- Ownership of decisions (whether users feel they own the decision made by AI) also impacts trust, with less ownership leading to higher trust.
Trustworthiness Technologies:
- Trust in AI can be supported by technologies ensuring fairness, explainability, auditability, and safety (FEAS).
- These technologies align with qualities such as ability, benevolence, integrity, and predictability, which are essential for building trust .
Appropriate Trust and Evaluation Methods:
- Appropriate trust in AI involves balancing confidence and skepticism, and can be fostered through confidence scores, explanations, and trustworthiness cues.
- Evaluating trust in AI requires methods that capture user vulnerability and verbalized thoughts to understand trust-related actions better.

Conclusion

Trust is a pivotal element in the adoption and effective use of AI technologies. It is influenced by a combination of user characteristics, environmental factors, and design elements. Effective communication of AI performance and the implementation of trustworthiness technologies are crucial for fostering appropriate trust. Understanding and evaluating trust through comprehensive methods can further enhance the relationship between humans and AI, leading to more successful integration of AI systems into everyday life.

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Communicating trust in AI

Summary

Introduction

Key Insights

Conclusion

Sources and full results

Trust in AI and Its Role in the Acceptance of AI Technologies

Trust in AI significantly influences users' intention to use AI technologies, with functionality-related trust having a greater impact on usage intention than human-like trust.

A Systematic Review on Fostering Appropriate Trust in Human-AI Interaction

This paper reviews current practices and challenges in building appropriate trust in human-AI interactions, highlighting potential gaps and research opportunities for future work.

Trust in Artificial Intelligence

Trust in AI progresses in the reverse order from human-human teams, with faith initially replaced by dependability and predictability, and design factors like anthrophomorphizing aiding in long-term social bonds.

Communicating the Limitations of AI: The Effect of Message Framing and Ownership on Trust in Artificial Intelligence

Effective communication about AI performance, including decision ownership and message framing, can promote trust in AI interactions.

The relationship between trust in AI and trustworthy machine learning technologies

This paper outlines the relationship between trust in AI and trustworthy machine learning technologies, focusing on Fairness, Explainability, Auditability, and Safety (FEAS).

In AI We Trust Incrementally: a Multi-layer Model of Trust to Analyze Human-Artificial Intelligence Interactions

This paper proposes an incremental model of trust for human-AI interactions, analyzing epistemic, pragmatic, and normative reasons for trust in human-AI interactions.

Can we trust AI? An empirical investigation of trust requirements and guide to successful AI adoption

Successful AI adoption requires access to knowledge, transparency, explainability, certification, and self-imposed standards and guidelines to increase overall trust in AI.

Modelling Trust in Human-AI Interaction

This research aims to develop formal and social models of trust to better understand human-AI interactions, and to help AI systems promote appropriate trust levels in personal support contexts.

Improving trust in data and algorithms in the medium of AI

To build trust in AI, technology creators should ensure accurate data and transparent algorithms, which are auditable and governed, to overcome the lack of trust among users, investors, and stakeholders.

Externalizing Vulnerability and Verbalizing Thoughts to More Adequately Evaluate Trust in AI

Externalizing vulnerability and verbalizing thoughts in experimental studies can better evaluate trust in AI systems, improving our understanding of user interactions.

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