# Trust in AI: Progress, Challenges, and Future Directions

arXiv: https://arxiv.org/abs/2403.14680 · HTML: https://mlsanity.com/paper/2403.14680/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 47 of its 288 references that are in the map.
- First posted on arXiv 12 Mar 2024.

## Builds on (most cited first, 30 of 47)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020)
- [Neural Additive Models: Interpretable Machine Learning with Neural Nets](https://mlsanity.com/paper/2004.13912/) (2020)
- [Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making](https://mlsanity.com/paper/2001.02114/) (2020)
- [Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey](https://mlsanity.com/paper/2006.11371/) (2020)
- [Techniques for Interpretable Machine Learning](https://mlsanity.com/paper/1808.00033/) (2018)
- [DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker](https://mlsanity.com/paper/1701.01724/) (2017)
- [Visualizing and Understanding Neural Models in NLP](https://mlsanity.com/paper/1506.01066/) (2015)
- [Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI](https://mlsanity.com/paper/2010.07487/) (2020)
- [Post-hoc Interpretability for Neural NLP: A Survey](https://mlsanity.com/paper/2108.04840/) (2021)
- [What do we need to build explainable AI systems for the medical domain?](https://mlsanity.com/paper/1712.09923/) (2017)
- [The Ethics of AI Ethics -- An Evaluation of Guidelines](https://mlsanity.com/paper/1903.03425/) (2019)
- [Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges](https://mlsanity.com/paper/2010.09337/) (2020)
- [To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making](https://mlsanity.com/paper/2102.09692/) (2021)
- [Artificial Intelligence: the global landscape of ethics guidelines](https://mlsanity.com/paper/1906.11668/) (2019)
- [Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences](https://mlsanity.com/paper/1712.00547/) (2017)
- [Interpretability of machine learning based prediction models in healthcare](https://mlsanity.com/paper/2002.08596/) (2020)
- [Quantifying Interpretability and Trust in Machine Learning Systems](https://mlsanity.com/paper/1901.08558/) (2019)
- [Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems](https://mlsanity.com/paper/1806.07552/) (2018)
- [How model accuracy and explanation fidelity influence user trust](https://mlsanity.com/paper/1907.12652/) (2019)
- [Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens](https://mlsanity.com/paper/2102.00593/) (2021)
- [Do Humans Trust Advice More if it Comes from AI? An Analysis of Human-AI Interactions](https://mlsanity.com/paper/2107.07015/) (2021)
- [Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward](https://mlsanity.com/paper/1905.12762/) (2019)
- [FactSheets: Increasing Trust in AI Services through Supplier's Declarations of Conformity](https://mlsanity.com/paper/1808.07261/) (2018)
- [Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability](https://mlsanity.com/paper/1904.03867/) (2019)
- [The Sanction of Authority: Promoting Public Trust in AI](https://mlsanity.com/paper/2102.04221/) (2021)

## Built on by (most cited first, 30 of 35)

- [Towards Trustworthy AI: A Review of Ethical and Robust Large Language Models](https://mlsanity.com/paper/2407.13934/) (2024)
- [Intelligent AI Delegation](https://mlsanity.com/paper/2602.11865/) (2026)
- [From tools to thieves: Measuring and understanding public perceptions of AI through crowdsourced metaphors](https://mlsanity.com/paper/2501.18045/) (2025)
- [DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey](https://mlsanity.com/paper/2503.09956/) (2025)
- [Position: Human-Centric AI Requires a Minimum Viable Level of Human Understanding](https://mlsanity.com/paper/2602.00854/) (2026)
- [A Framework for Responsible AI Systems: Building Societal Trust through Domain Definition, Trustworthy AI Design, Auditability, Accountability, and Governance](https://mlsanity.com/paper/2503.04739/) (2025)
- [Understanding Human-AI Trust in Education](https://mlsanity.com/paper/2506.09160/) (2025)
- [Selecting the Right LLM for eGov Explanations](https://mlsanity.com/paper/2504.21032/) (2025)
- [A Framework for Longitudinal Health AI Agents](https://mlsanity.com/paper/2604.12019/) (2026)
- [Explainability of Large Language Models: Opportunities and Challenges toward Generating Trustworthy Explanations](https://mlsanity.com/paper/2510.17256/) (2025)
- [Multi-Stakeholder Alignment in LLM-Powered Collaborative AI Systems: A Multi-Agent Framework for Intelligent Tutoring](https://mlsanity.com/paper/2510.23245/) (2025)
- [Beyond Chat: a Framework for LLMs as Human-Centered Support Systems](https://mlsanity.com/paper/2511.03729/) (2025)
- [Calibrated Trust in Dealing with LLM Hallucinations: A Qualitative Study](https://mlsanity.com/paper/2512.09088/) (2025)
- [Confirmation Bias in Generative AI Chatbots: Mechanisms, Risks, Mitigation Strategies, and Future Research Directions](https://mlsanity.com/paper/2504.09343/) (2025)
- [Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences (October 2025 -- Version 2)](https://mlsanity.com/paper/2505.16619/) (2025)
- [Agentic AI with Orchestrator-Agent Trust: A Modular Visual Classification Framework with Trust-Aware Orchestration and RAG-Based Reasoning](https://mlsanity.com/paper/2507.10571/) (2025)
- [DoubleAgents: Human-Agent Alignment in a Socially Embedded Workflow](https://mlsanity.com/paper/2509.12626/) (2025)
- [Plausibility as Failure: How LLMs and Humans Co-Construct Epistemic Error](https://mlsanity.com/paper/2512.16750/) (2025)
- [Beyond Abstract Compliance: Operationalising trust in AI as a moral relationship](https://mlsanity.com/paper/2601.22769/) (2026)
- [An Expert Schema for Evaluating Large Language Model Errors in Scholarly Question-Answering Systems](https://mlsanity.com/paper/2602.21059/) (2026)
- [Examining Users' Behavioural Intention to Use OpenClaw Through the Cognition--Affect--Conation Framework](https://mlsanity.com/paper/2603.11455/) (2026)
- [Towards Computer-Using Personal Agents](https://mlsanity.com/paper/2503.15515/) (2025)
- [From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0](https://mlsanity.com/paper/2507.15796/) (2025)
- [Preliminary Quantitative Study on Explainability and Trust in AI Systems](https://mlsanity.com/paper/2510.15769/) (2025)
- [Adaptive Trust Metrics for Multi-LLM Systems: Enhancing Reliability in Regulated Industries](https://mlsanity.com/paper/2601.08858/) (2026)
- [Eliciting Trustworthiness Priors of Large Language Models via Economic Games](https://mlsanity.com/paper/2602.00769/) (2026)
- [Botson: An Accessible and Low-Cost Platform for Social Robotics Research](https://mlsanity.com/paper/2602.19491/) (2026)
- [Reciprocal Trust and Distrust in Artificial Intelligence Systems: The Hard Problem of Regulation](https://mlsanity.com/paper/2604.05826/) (2026)
- [Trust the AI, Doubt Yourself: The Effect of Urgency on Self-Confidence in Human-AI Interaction](https://mlsanity.com/paper/2604.07535/) (2026)
- [Seeking Information with RAG-Assistants: Does Model Size Matter in Human-AI Collaborations?](https://mlsanity.com/paper/2605.00964/) (2026)

## Abstract (arXiv)

The increasing use of artificial intelligence (AI) systems in our daily life through various applications, services, and products explains the significance of trust/distrust in AI from a user perspective. AI-driven systems (as opposed to other technologies) have ubiquitously diffused in our life not only as some beneficial tools to be used by human agents but also are going to be substitutive agents on our behalf, or manipulative minds that would influence human thought, decision, and agency. Trust/distrust in AI plays the role of a regulator and could significantly control the level of this diffusion, as trust can increase, and distrust may reduce the rate of adoption of AI. Recently, varieties of studies have paid attention to the variant dimension of trust/distrust in AI, and its relevant considerations. In this systematic literature review, after conceptualization of trust in the current AI literature review, we will investigate trust in different types of human-Machine interaction, and its impact on technology acceptance in different domains. In addition to that, we propose a taxonomy of technical (i.e., safety, accuracy, robustness) and non-technical axiological (i.e., ethical, legal, and mixed) trustworthiness metrics, and some trustworthy measurements. Moreover, we examine some major trust-breakers in AI (e.g., autonomy and dignity threat), and trust makers; and propose some future directions and probable solutions for the transition to a trustworthy AI.