# The relationship between trust in AI and trustworthy machine learning technologies

arXiv: https://arxiv.org/abs/1912.00782 · HTML: https://mlsanity.com/paper/1912.00782/

## Key facts

- Cited by 78 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2019.

## Built on by (most cited first, 30 of 78)

- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [Instruction Backdoor Attacks Against Customized LLMs](https://mlsanity.com/paper/2402.09179/) (2024)
- [Explanation-Based Human Debugging of NLP Models: A Survey](https://mlsanity.com/paper/2104.15135/) (2021)
- [Designing for Responsible Trust in AI Systems: A Communication Perspective](https://mlsanity.com/paper/2204.13828/) (2022)
- [Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations](https://mlsanity.com/paper/2205.07277/) (2022)
- [A Review on Machine Unlearning](https://mlsanity.com/paper/2411.11315/) (2024)
- [The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations](https://mlsanity.com/paper/2212.11737/) (2022)
- [AI-Augmented Business Process Management Systems: A Research Manifesto](https://mlsanity.com/paper/2201.12855/) (2022)
- [The Eval4NLP Shared Task on Explainable Quality Estimation: Overview and Results](https://mlsanity.com/paper/2110.04392/) (2021)
- [A Systematic Review on Fostering Appropriate Trust in Human-AI Interaction](https://mlsanity.com/paper/2311.06305/) (2023)
- [Learning to run a power network with trust](https://mlsanity.com/paper/2110.12908/) (2021)
- [Can Explainable AI Explain Unfairness? A Framework for Evaluating Explainable AI](https://mlsanity.com/paper/2106.07483/) (2021)
- [A Survey on AI Assurance](https://mlsanity.com/paper/2111.07505/) (2021)
- [Towards Explainable Evaluation Metrics for Natural Language Generation](https://mlsanity.com/paper/2203.11131/) (2022)
- [Towards Explainable Evaluation Metrics for Machine Translation](https://mlsanity.com/paper/2306.13041/) (2023)
- [Humans, AI, and Context: Understanding End-Users' Trust in a Real-World Computer Vision Application](https://mlsanity.com/paper/2305.08598/) (2023)
- [Trust in AI: Interpretability is not necessary or sufficient, while black-box interaction is necessary and sufficient](https://mlsanity.com/paper/2202.05302/) (2022)
- [A Survey of Controllable Learning: Methods and Applications in Information Retrieval](https://mlsanity.com/paper/2407.06083/) (2024)
- ["Explain it in the Same Way!" -- Model-Agnostic Group Fairness of Counterfactual Explanations](https://mlsanity.com/paper/2211.14858/) (2022)
- [Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective](https://mlsanity.com/paper/2401.04374/) (2024)
- [Toward a Taxonomy of Trust for Probabilistic Machine Learning](https://mlsanity.com/paper/2112.03270/) (2021)
- [Evaluating Cultural Awareness of LLMs for Yoruba, Malayalam, and English](https://mlsanity.com/paper/2410.01811/) (2024)
- [Out-of-distribution detection algorithms for robust insect classification](https://mlsanity.com/paper/2305.01823/) (2023)
- [Interpretable Machine Learning for Discovery: Statistical Challenges & Opportunities](https://mlsanity.com/paper/2308.01475/) (2023)
- [Verifiable Fairness: Privacy-preserving Computation of Fairness for Machine Learning Systems](https://mlsanity.com/paper/2309.06061/) (2023)
- [Discussing the Spectrum of Physics-Enhanced Machine Learning; a Survey on Structural Mechanics Applications](https://mlsanity.com/paper/2310.20425/) (2023)
- [Latent Space Interpretation for Stylistic Analysis and Explainable Authorship Attribution](https://mlsanity.com/paper/2409.07072/) (2024)
- [To Err Is AI! Debugging as an Intervention to Facilitate Appropriate Reliance on AI Systems](https://mlsanity.com/paper/2409.14377/) (2024)
- [Interactive Machine Learning: A State of the Art Review](https://mlsanity.com/paper/2207.06196/) (2022)
- [A Systematic Literature Review of Human-Centered, Ethical, and Responsible AI](https://mlsanity.com/paper/2302.05284/) (2023)

## Abstract (arXiv)

To build AI-based systems that users and the public can justifiably trust one needs to understand how machine learning technologies impact trust put in these services. To guide technology developments, this paper provides a systematic approach to relate social science concepts of trust with the technologies used in AI-based services and products. We conceive trust as discussed in the ABI (Ability, Benevolence, Integrity) framework and use a recently proposed mapping of ABI on qualities of technologies. We consider four categories of machine learning technologies, namely these for Fairness, Explainability, Auditability and Safety (FEAS) and discuss if and how these possess the required qualities. Trust can be impacted throughout the life cycle of AI-based systems, and we introduce the concept of Chain of Trust to discuss technological needs for trust in different stages of the life cycle. FEAS has obvious relations with known frameworks and therefore we relate FEAS to a variety of international Principled AI policy and technology frameworks that have emerged in recent years.