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

arXiv: https://arxiv.org/abs/2311.06305 · HTML: https://mlsanity.com/paper/2311.06305/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 218 references that are in the map.
- First posted on arXiv 8 Nov 2023.

## Builds on (most cited first, 25 of 25)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images](https://mlsanity.com/paper/1412.1897/) (2014)
- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI](https://mlsanity.com/paper/2201.08164/) (2022)
- [Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making](https://mlsanity.com/paper/2001.02114/) (2020)
- [Manipulating and Measuring Model Interpretability](https://mlsanity.com/paper/1802.07810/) (2018)
- [Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI](https://mlsanity.com/paper/2010.07487/) (2020)
- [On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection](https://mlsanity.com/paper/1811.07901/) (2018)
- [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)
- ["How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations](https://mlsanity.com/paper/1911.06473/) (2019)
- ["Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans](https://mlsanity.com/paper/2001.05871/) (2020)
- [Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanations](https://mlsanity.com/paper/2302.02187/) (2023)
- [The relationship between trust in AI and trustworthy machine learning technologies](https://mlsanity.com/paper/1912.00782/) (2019)
- [Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making](https://mlsanity.com/paper/2301.05809/) (2023)
- [Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision support](https://mlsanity.com/paper/2302.12389/) (2023)
- [Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Making](https://mlsanity.com/paper/2101.05303/) (2021)
- [Establishing Appropriate Trust via Critical States](https://mlsanity.com/paper/1810.08174/) (2018)
- [Designing for Responsible Trust in AI Systems: A Communication Perspective](https://mlsanity.com/paper/2204.13828/) (2022)
- [Ignore, Trust, or Negotiate: Understanding Clinician Acceptance of AI-Based Treatment Recommendations in Health Care](https://mlsanity.com/paper/2302.00096/) (2023)
- [Modeling and Predicting Trust Dynamics in Human-Robot Teaming: A Bayesian Inference Approach](https://mlsanity.com/paper/2007.13151/) (2020)
- [Are we measuring trust correctly in explainability, interpretability, and transparency research?](https://mlsanity.com/paper/2209.00651/) (2022)
- [Toward Adaptive Trust Calibration for Level 2 Driving Automation](https://mlsanity.com/paper/2009.11890/) (2020)
- [An Investigation of Drivers' Dynamic Situational Trust in Conditionally Automated Driving](https://mlsanity.com/paper/2112.04095/) (2021)
- [A Mental Model Based Framework of Trust](https://mlsanity.com/paper/2301.12569/) (2023)
- [Human-centered trust framework: An HCI perspective](https://mlsanity.com/paper/2305.03306/) (2023)

## Built on by (most cited first, 21 of 21)

- [Plan-Then-Execute: An Empirical Study of User Trust and Team Performance When Using LLM Agents As A Daily Assistant](https://mlsanity.com/paper/2502.01390/) (2025)
- [From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents](https://mlsanity.com/paper/2506.18959/) (2025)
- [Agentic AI Software Engineers: Programming with Trust](https://mlsanity.com/paper/2502.13767/) (2025)
- [AI, Help Me Thinkunicodex2014but for Myself: Assisting People in Complex Decision-Making by Providing Different Kinds of Cognitive Support](https://mlsanity.com/paper/2504.06771/) (2025)
- [Understanding AI Trustworthiness: A Scoping Review of AIES & FAccT Articles](https://mlsanity.com/paper/2510.21293/) (2025)
- [Human-Centered AI Communication in Co-Creativity: An Initial Framework and Insights](https://mlsanity.com/paper/2505.18385/) (2025)
- [Improving User Experience with FAICO: Towards a Framework for AI Communication in Human-AI Co-Creativity](https://mlsanity.com/paper/2504.02526/) (2025)
- [Classifying Epistemic Relationships in Human-AI Interaction: An Exploratory Approach](https://mlsanity.com/paper/2508.03673/) (2025)
- [Contextualized AI for Cyber Defense: An Automated Survey using LLMs](https://mlsanity.com/paper/2409.13524/) (2024)
- [What is Safety? Corporate Discourse, Power, and the Politics of Generative AI Safety](https://mlsanity.com/paper/2602.06981/) (2026)
- [Empowering 9-1-1 Calltaking Training with Generative AI: Experiences and Lessons Learned](https://mlsanity.com/paper/2602.13241/) (2026)
- [A Methodology for Auditable Trustworthiness Levels in AI Lifecycle Governance](https://mlsanity.com/paper/2607.16130/) (2026)
- [Once Upon an AI: Six Scaffolds for Child-AI Interaction Design, Inspired by Disney](https://mlsanity.com/paper/2504.08670/) (2025)
- ["Even explanations will not help in trusting [this] fundamentally biased system": A Predictive Policing Case-Study](https://mlsanity.com/paper/2504.11020/) (2025)
- [Barrier Function Overrides For Non-Convex Fixed Wing Flight Control and Self-Driving Cars](https://mlsanity.com/paper/2505.05548/) (2025)
- [Conformal Prediction and Trustworthy AI](https://mlsanity.com/paper/2508.06885/) (2025)
- [In Generative AI We (Dis)Trust? Computational Analysis of Trust and Distrust in Reddit Discussions](https://mlsanity.com/paper/2510.16173/) (2025)
- [Does In-IDE Calibration of Large Language Models work at Scale?](https://mlsanity.com/paper/2510.22614/) (2025)
- [When Life Gives You AI, Will You Turn It Into A Market for Lemons? Understanding How Information Asymmetries About AI System Capabilities Affect Market Outcomes and Adoption](https://mlsanity.com/paper/2601.21650/) (2026)
- ["I Don't Know" -- Towards Appropriate Trust with Certainty-Aware Retrieval Augmented Generation](https://mlsanity.com/paper/2605.00957/) (2026)
- [Engagement Intensity as a Learner-Modeling Signal for Adaptive AI Ethics Instruction](https://mlsanity.com/paper/2606.18548/) (2026)

## Abstract (arXiv)

Appropriate Trust in Artificial Intelligence (AI) systems has rapidly become an important area of focus for both researchers and practitioners. Various approaches have been used to achieve it, such as confidence scores, explanations, trustworthiness cues, or uncertainty communication. However, a comprehensive understanding of the field is lacking due to the diversity of perspectives arising from various backgrounds that influence it and the lack of a single definition for appropriate trust. To investigate this topic, this paper presents a systematic review to identify current practices in building appropriate trust, different ways to measure it, types of tasks used, and potential challenges associated with it. We also propose a Belief, Intentions, and Actions (BIA) mapping to study commonalities and differences in the concepts related to appropriate trust by (a) describing the existing disagreements on defining appropriate trust, and (b) providing an overview of the concepts and definitions related to appropriate trust in AI from the existing literature. Finally, the challenges identified in studying appropriate trust are discussed, and observations are summarized as current trends, potential gaps, and research opportunities for future work. Overall, the paper provides insights into the complex concept of appropriate trust in human-AI interaction and presents research opportunities to advance our understanding on this topic.