# Designing for Responsible Trust in AI Systems: A Communication Perspective

arXiv: https://arxiv.org/abs/2204.13828 · HTML: https://mlsanity.com/paper/2204.13828/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 72 references that are in the map.
- First posted on arXiv 29 Apr 2022.

## Builds on (most cited first, 16 of 16)

- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020)
- [Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making](https://mlsanity.com/paper/2001.02114/) (2020)
- [Metrics for Explainable AI: Challenges and Prospects](https://mlsanity.com/paper/1812.04608/) (2018)
- [Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI](https://mlsanity.com/paper/2010.07487/) (2020)
- [Principles alone cannot guarantee ethical AI](https://mlsanity.com/paper/1906.06668/) (2019)
- [Expanding Explainability: Towards Social Transparency in AI systems](https://mlsanity.com/paper/2101.04719/) (2021)
- [Human-Centered Explainable AI (XAI): From Algorithms to User Experiences](https://mlsanity.com/paper/2110.10790/) (2021)
- [Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies](https://mlsanity.com/paper/2112.11471/) (2021)
- [The relationship between trust in AI and trustworthy machine learning technologies](https://mlsanity.com/paper/1912.00782/) (2019)
- [Misplaced Trust: Measuring the Interference of Machine Learning in Human Decision-Making](https://mlsanity.com/paper/2005.10960/) (2020)
- [The Sanction of Authority: Promoting Public Trust in AI](https://mlsanity.com/paper/2102.04221/) (2021)
- [Experiences with Improving the Transparency of AI Models and Services](https://mlsanity.com/paper/1911.08293/) (2019)

## Built on by (most cited first, 30 of 48)

- [Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences](https://mlsanity.com/paper/2404.12272/) (2024)
- [Homogenization Effects of Large Language Models on Human Creative Ideation](https://mlsanity.com/paper/2402.01536/) (2024)
- [AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap](https://mlsanity.com/paper/2306.01941/) (2023)
- [ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testing](https://mlsanity.com/paper/2309.09128/) (2023)
- [Better to Ask in English: Cross-Lingual Evaluation of Large Language Models for Healthcare Queries](https://mlsanity.com/paper/2310.13132/) (2023)
- [Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations](https://mlsanity.com/paper/2301.07255/) (2023)
- [Calibrating Large Language Models Using Their Generations Only](https://mlsanity.com/paper/2403.05973/) (2024)
- [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)
- [Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistencies](https://mlsanity.com/paper/2502.08554/) (2025)
- [Selective Explanations: Leveraging Human Input to Align Explainable AI](https://mlsanity.com/paper/2301.09656/) (2023)
- [Towards interactive evaluations for interaction harms in human-AI systems](https://mlsanity.com/paper/2405.10632/) (2024)
- [A Systematic Review on Fostering Appropriate Trust in Human-AI Interaction](https://mlsanity.com/paper/2311.06305/) (2023)
- [Humans, AI, and Context: Understanding End-Users' Trust in a Real-World Computer Vision Application](https://mlsanity.com/paper/2305.08598/) (2023)
- [Social Intelligence Data Infrastructure: Structuring the Present and Navigating the Future](https://mlsanity.com/paper/2403.14659/) (2024)
- [Characterizing and modeling harms from interactions with design patterns in AI interfaces](https://mlsanity.com/paper/2404.11370/) (2024)
- [Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations](https://mlsanity.com/paper/2407.08983/) (2024)
- [A Systematic Literature Review of Human-Centered, Ethical, and Responsible AI](https://mlsanity.com/paper/2302.05284/) (2023)
- [Understanding AI Trustworthiness: A Scoping Review of AIES & FAccT Articles](https://mlsanity.com/paper/2510.21293/) (2025)
- [Certification Labels for Trustworthy AI: Insights From an Empirical Mixed-Method Study](https://mlsanity.com/paper/2305.18307/) (2023)
- [(Un)making AI Magic: a Design Taxonomy](https://mlsanity.com/paper/2403.15216/) (2024)
- [An Empirical Exploration of Trust Dynamics in LLM Supply Chains](https://mlsanity.com/paper/2405.16310/) (2024)
- [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)
- [Thoughtful Adoption of NLP for Civic Participation: Understanding Differences Among Policymakers](https://mlsanity.com/paper/2410.22937/) (2024)
- [Label Effects: Shared Heuristic Reliance in Trust Assessment by Humans and LLM-as-a-Judge](https://mlsanity.com/paper/2604.05593/) (2026)
- [The AI-DEC: A Card-based Design Method for User-centered AI Explanations](https://mlsanity.com/paper/2405.16711/) (2024)
- [Predicting Trust In Autonomous Vehicles: Modeling Young Adult Psychosocial Traits, Risk-Benefit Attitudes, And Driving Factors With Machine Learning](https://mlsanity.com/paper/2409.08980/) (2024)
- [Contextualized AI for Cyber Defense: An Automated Survey using LLMs](https://mlsanity.com/paper/2409.13524/) (2024)
- [Mapping Trustworthiness in Large Language Models: A Bibliometric Analysis Bridging Theory to Practice](https://mlsanity.com/paper/2503.04785/) (2025)
- [The Variance Paradox: How AI Reduces Diversity but Increases Novelty](https://mlsanity.com/paper/2508.19264/) (2025)
- [NeuroBridge: Using Generative AI to Bridge Cross-neurotype Communication Differences through Neurotypical Perspective-taking](https://mlsanity.com/paper/2509.23434/) (2025)

## Abstract (arXiv)

Current literature and public discourse on "trust in AI" are often focused on the principles underlying trustworthy AI, with insufficient attention paid to how people develop trust. Given that AI systems differ in their level of trustworthiness, two open questions come to the fore: how should AI trustworthiness be responsibly communicated to ensure appropriate and equitable trust judgments by different users, and how can we protect users from deceptive attempts to earn their trust? We draw from communication theories and literature on trust in technologies to develop a conceptual model called MATCH, which describes how trustworthiness is communicated in AI systems through trustworthiness cues and how those cues are processed by people to make trust judgments. Besides AI-generated content, we highlight transparency and interaction as AI systems' affordances that present a wide range of trustworthiness cues to users. By bringing to light the variety of users' cognitive processes to make trust judgments and their potential limitations, we urge technology creators to make conscious decisions in choosing reliable trustworthiness cues for target users and, as an industry, to regulate this space and prevent malicious use. Towards these goals, we define the concepts of warranted trustworthiness cues and expensive trustworthiness cues, and propose a checklist of requirements to help technology creators identify appropriate cues to use. We present a hypothetical use case to illustrate how practitioners can use MATCH to design AI systems responsibly, and discuss future directions for research and industry efforts aimed at promoting responsible trust in AI.