# In AI We Trust? Factors That Influence Trustworthiness of AI-infused Decision-Making Processes

arXiv: https://arxiv.org/abs/1912.02675 · HTML: https://mlsanity.com/paper/1912.02675/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2019.

## Built on by (most cited first, 13 of 13)

- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [Design Principles for Generative AI Applications](https://mlsanity.com/paper/2401.14484/) (2024)
- [Trust in AI: Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2403.14680/) (2024)
- [Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI](https://mlsanity.com/paper/2302.00799/) (2023)
- [A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspective](https://mlsanity.com/paper/2304.08795/) (2023)
- [Towards a Comprehensive Human-Centred Evaluation Framework for Explainable AI](https://mlsanity.com/paper/2308.06274/) (2023)
- [DCNFIS: Deep Convolutional Neuro-Fuzzy Inference System](https://mlsanity.com/paper/2308.06378/) (2023)
- [Calibrated Trust in Dealing with LLM Hallucinations: A Qualitative Study](https://mlsanity.com/paper/2512.09088/) (2025)
- [Test-takers have a say: understanding the implications of the use of AI in language tests](https://mlsanity.com/paper/2307.09885/) (2023)
- [Tell Me Something That Will Help Me Trust You: A Survey of Trust Calibration in Human-Agent Interaction](https://mlsanity.com/paper/2205.02987/) (2022)
- [Why not both? Complementing explanations with uncertainty, and the role of self-confidence in Human-AI collaboration](https://mlsanity.com/paper/2304.14130/) (2023)
- [Uncertainty-Aware Explanations Through Probabilistic Self-Explainable Neural Networks](https://mlsanity.com/paper/2403.13740/) (2024)
- [Words to Describe What I'm Feeling: Exploring the Potential of AI Agents for High Subjectivity Decisions in Advance Care Planning](https://mlsanity.com/paper/2512.11276/) (2025)

## 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.