In AI We Trust? Factors That Influence Trustworthiness of AI-infused Decision-Making Processes
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Abstract (arXiv)
Many decision-making processes have begun to incorporate an AI element, including prison sentence recommendations, college admissions, hiring, and mortgage approval. In all of these cases, AI models are being trained to help human decision makers reach accurate and fair judgments, but little is known about what factors influence the extent to which people consider an AI-infused decision-making process to be trustworthy. We aim to understand how different factors about a decision-making process, and an AI model that supports that process, influences peoples' perceptions of the trustworthiness of that process. We report on our evaluation of how seven different factors -- decision stakes, decision authority, model trainer, model interpretability, social transparency, and model confidence -- influence ratings of trust in a scenario-based study.
Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Dec 2019.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 6; 2024: 3; 2025: 2.
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- 2021Interpretable Machine Learning: Fundamental Principles and 10 Grand Challengescited by 367
- 2024Design Principles for Generative AI Applicationscited by 57
- 2024Trust in AI: Progress, Challenges, and Future Directionscited by 35
- 2023Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAIcited by 33
- 2023A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspectivecited by 33
- 2023Towards a Comprehensive Human-Centred Evaluation Framework for Explainable AIcited by 3
- 2023DCNFIS: Deep Convolutional Neuro-Fuzzy Inference Systemcited by 2
- 2025Calibrated Trust in Dealing with LLM Hallucinations: A Qualitative Studycited by 2
- 2023Test-takers have a say: understanding the implications of the use of AI in language testscited by 1
- 2022Tell Me Something That Will Help Me Trust You: A Survey of Trust Calibration in Human-Agent Interactioncited by 0
- 2023Why not both? Complementing explanations with uncertainty, and the role of self-confidence in Human-AI collaborationcited by 0
- 2024Uncertainty-Aware Explanations Through Probabilistic Self-Explainable Neural Networkscited by 0
- 2025Words to Describe What I'm Feeling: Exploring the Potential of AI Agents for High Subjectivity Decisions in Advance Care Planning12 Dec 2025
- 2025Calibrated Trust in Dealing with LLM Hallucinations: A Qualitative Study9 Dec 2025
- 2024Uncertainty-Aware Explanations Through Probabilistic Self-Explainable Neural Networks20 Mar 2024
- 2024Trust in AI: Progress, Challenges, and Future Directions12 Mar 2024
- 2024Design Principles for Generative AI Applications25 Jan 2024
- 2023DCNFIS: Deep Convolutional Neuro-Fuzzy Inference System11 Aug 2023
- 2023Towards a Comprehensive Human-Centred Evaluation Framework for Explainable AI31 Jul 2023
- 2023Test-takers have a say: understanding the implications of the use of AI in language tests19 Jul 2023
- 2023Why not both? Complementing explanations with uncertainty, and the role of self-confidence in Human-AI collaboration27 Apr 2023
- 2023A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspective18 Apr 2023
- 2023Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI1 Feb 2023
- 2022Tell Me Something That Will Help Me Trust You: A Survey of Trust Calibration in Human-Agent Interaction6 May 2022
1 more
- 2021Interpretable Machine Learning: Fundamental Principles and 10 Grand Challengescited by 367
- 2024Design Principles for Generative AI Applicationscited by 57
- 2024Trust in AI: Progress, Challenges, and Future Directionscited by 35
- 2023Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAIcited by 33
- 2023A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspectivecited by 33
- 2023Towards a Comprehensive Human-Centred Evaluation Framework for Explainable AIcited by 3
- 2023DCNFIS: Deep Convolutional Neuro-Fuzzy Inference Systemcited by 2
- 2025Calibrated Trust in Dealing with LLM Hallucinations: A Qualitative Studycited by 2
- 2023Test-takers have a say: understanding the implications of the use of AI in language testscited by 1
- 2022Tell Me Something That Will Help Me Trust You: A Survey of Trust Calibration in Human-Agent Interactioncited by 0
- 2023Why not both? Complementing explanations with uncertainty, and the role of self-confidence in Human-AI collaborationcited by 0
- 2024Uncertainty-Aware Explanations Through Probabilistic Self-Explainable Neural Networkscited by 0
12 most cited of 13 All 13 (JSON)
- 2025Words to Describe What I'm Feeling: Exploring the Potential of AI Agents for High Subjectivity Decisions in Advance Care Planning12 Dec 2025
- 2025Calibrated Trust in Dealing with LLM Hallucinations: A Qualitative Study9 Dec 2025
- 2024Uncertainty-Aware Explanations Through Probabilistic Self-Explainable Neural Networks20 Mar 2024
- 2024Trust in AI: Progress, Challenges, and Future Directions12 Mar 2024
- 2024Design Principles for Generative AI Applications25 Jan 2024
- 2023DCNFIS: Deep Convolutional Neuro-Fuzzy Inference System11 Aug 2023
- 2023Towards a Comprehensive Human-Centred Evaluation Framework for Explainable AI31 Jul 2023
- 2023Test-takers have a say: understanding the implications of the use of AI in language tests19 Jul 2023
- 2023Why not both? Complementing explanations with uncertainty, and the role of self-confidence in Human-AI collaboration27 Apr 2023
- 2023A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspective18 Apr 2023
- 2023Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI1 Feb 2023
- 2022Tell Me Something That Will Help Me Trust You: A Survey of Trust Calibration in Human-Agent Interaction6 May 2022
12 newest of 13 All 13 (JSON)