# "There Is Not Enough Information": On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Making

arXiv: https://arxiv.org/abs/2205.05758 · HTML: https://mlsanity.com/paper/2205.05758/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 134 references that are in the map.
- First posted on arXiv 11 May 2022.

## Builds on (most cited first, 29 of 29)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [Fairness in Machine Learning: A Survey](https://mlsanity.com/paper/2010.04053/) (2020)
- [Fairness in Learning: Classic and Contextual Bandits](https://mlsanity.com/paper/1605.07139/) (2016)
- [Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI](https://mlsanity.com/paper/2010.07487/) (2020)
- [One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques](https://mlsanity.com/paper/1909.03012/) (2019)
- [What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research](https://mlsanity.com/paper/2102.07817/) (2021)
- ['It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions](https://mlsanity.com/paper/1801.10408/) (2018)
- [A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores](https://mlsanity.com/paper/2002.08035/) (2020)
- [How Do Fairness Definitions Fare? Examining Public Attitudes Towards Algorithmic Definitions of Fairness](https://mlsanity.com/paper/1811.03654/) (2018)
- [Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differences](https://mlsanity.com/paper/2001.09604/) (2020)
- [Explainability Pitfalls: Beyond Dark Patterns in Explainable AI](https://mlsanity.com/paper/2109.12480/) (2021)
- [Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction](https://mlsanity.com/paper/1802.09548/) (2018)
- [This Thing Called Fairness: Disciplinary Confusion Realizing a Value in Technology](https://mlsanity.com/paper/1909.11869/) (2019)
- [Auditing for Discrimination in Algorithms Delivering Job Ads](https://mlsanity.com/paper/2104.04502/) (2021)
- [Deontological Ethics By Monotonicity Shape Constraints](https://mlsanity.com/paper/2001.11990/) (2020)
- [On the Relation of Trust and Explainability: Why to Engineer for Trustworthiness](https://mlsanity.com/paper/2108.05379/) (2021)
- [Dimensions of Diversity in Human Perceptions of Algorithmic Fairness](https://mlsanity.com/paper/2005.00808/) (2020)
- [Do you comply with AI? -- Personalized explanations of learning algorithms and their impact on employees' compliance behavior](https://mlsanity.com/paper/2002.08777/) (2020)
- [Fairness in machine learning: against false positive rate equality as a measure of fairness](https://mlsanity.com/paper/2007.02890/) (2020)
- [Appropriate Fairness Perceptions? On the Effectiveness of Explanations in Enabling People to Assess the Fairness of Automated Decision Systems](https://mlsanity.com/paper/2108.06500/) (2021)
- [The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good](https://mlsanity.com/paper/1612.00323/) (2016)
- [A Ranking Approach to Fair Classification](https://mlsanity.com/paper/2102.04565/) (2021)
- [Fairness and Decision-making in Collaborative Shift Scheduling Systems](https://mlsanity.com/paper/2001.09755/) (2020)
- [Perceptions of Fairness and Trustworthiness Based on Explanations in Human vs. Automated Decision-Making](https://mlsanity.com/paper/2109.05792/) (2021)

## Built on by (most cited first, 19 of 19)

- [Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanations](https://mlsanity.com/paper/2210.11584/) (2022)
- [Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Making](https://mlsanity.com/paper/2209.11812/) (2022)
- [A Critical Survey on Fairness Benefits of Explainable AI](https://mlsanity.com/paper/2310.13007/) (2023)
- [Bridging the Transparency Gap: What Can Explainable AI Learn From the AI Act?](https://mlsanity.com/paper/2302.10766/) (2023)
- [Mind the Gap! Bridging Explainable Artificial Intelligence and Human Understanding with Luhmann's Functional Theory of Communication](https://mlsanity.com/paper/2302.03460/) (2023)
- [Training Novices: The Role of Human-AI Collaboration and Knowledge Transfer](https://mlsanity.com/paper/2207.00497/) (2022)
- [Flexible and Robust Counterfactual Explanations with Minimal Satisfiable Perturbations](https://mlsanity.com/paper/2309.04676/) (2023)
- [Understanding AI Trustworthiness: A Scoping Review of AIES & FAccT Articles](https://mlsanity.com/paper/2510.21293/) (2025)
- [Information That Matters: Exploring Information Needs of People Affected by Algorithmic Decisions](https://mlsanity.com/paper/2401.13324/) (2024)
- [Heterogeneous Graph Neural Networks with Post-hoc Explanations for Multi-modal and Explainable Land Use Inference](https://mlsanity.com/paper/2406.13724/) (2024)
- [Human-Centered AI Communication in Co-Creativity: An Initial Framework and Insights](https://mlsanity.com/paper/2505.18385/) (2025)
- [Legally Binding but Unfair? Towards Assessing Fairness of Privacy Policies](https://mlsanity.com/paper/2403.08115/) (2024)
- [From Model Performance to Claim: How a Change of Focus in Machine Learning Replicability Can Help Bridge the Responsibility Gap](https://mlsanity.com/paper/2404.13131/) (2024)
- [Mapping the Potential of Explainable AI for Fairness Along the AI Lifecycle](https://mlsanity.com/paper/2404.18736/) (2024)
- [(Unfair) Norms in Fairness Research: A Meta-Analysis](https://mlsanity.com/paper/2407.16895/) (2024)
- [Exploring the Impact of Explainable AI and Cognitive Capabilities on Users' Decisions](https://mlsanity.com/paper/2505.01192/) (2025)
- ["Even explanations will not help in trusting [this] fundamentally biased system": A Predictive Policing Case-Study](https://mlsanity.com/paper/2504.11020/) (2025)
- [Alternative Fairness and Accuracy Optimization in Criminal Justice](https://mlsanity.com/paper/2511.04505/) (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)

## Abstract (arXiv)

Automated decision systems (ADS) are increasingly used for consequential decision-making. These systems often rely on sophisticated yet opaque machine learning models, which do not allow for understanding how a given decision was arrived at. In this work, we conduct a human subject study to assess people's perceptions of informational fairness (i.e., whether people think they are given adequate information on and explanation of the process and its outcomes) and trustworthiness of an underlying ADS when provided with varying types of information about the system. More specifically, we instantiate an ADS in the area of automated loan approval and generate different explanations that are commonly used in the literature. We randomize the amount of information that study participants get to see by providing certain groups of people with the same explanations as others plus additional explanations. From our quantitative analyses, we observe that different amounts of information as well as people's (self-assessed) AI literacy significantly influence the perceived informational fairness, which, in turn, positively relates to perceived trustworthiness of the ADS. A comprehensive analysis of qualitative feedback sheds light on people's desiderata for explanations, among which are (i) consistency (both with people's expectations and across different explanations), (ii) disclosure of monotonic relationships between features and outcome, and (iii) actionability of recommendations.