# Appropriate Fairness Perceptions? On the Effectiveness of Explanations in Enabling People to Assess the Fairness of Automated Decision Systems

arXiv: https://arxiv.org/abs/2108.06500 · HTML: https://mlsanity.com/paper/2108.06500/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 40 references that are in the map.
- First posted on arXiv 14 Aug 2021.

## Builds on (most cited first, 15 of 15)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [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)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016)
- [Algorithmic Recourse: from Counterfactual Explanations to Interventions](https://mlsanity.com/paper/2002.06278/) (2020)
- [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)
- [Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differences](https://mlsanity.com/paper/2001.09604/) (2020)
- [Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction](https://mlsanity.com/paper/1802.09548/) (2018)
- [Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgment](https://mlsanity.com/paper/1901.07694/) (2019)
- [This Thing Called Fairness: Disciplinary Confusion Realizing a Value in Technology](https://mlsanity.com/paper/1909.11869/) (2019)
- [A Study on Fairness and Trust Perceptions in Automated Decision Making](https://mlsanity.com/paper/2103.04757/) (2021)
- [A Ranking Approach to Fair Classification](https://mlsanity.com/paper/2102.04565/) (2021)

## Built on by (most cited first, 9 of 9)

- [Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanations](https://mlsanity.com/paper/2210.11584/) (2022)
- [Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI](https://mlsanity.com/paper/2302.00799/) (2023)
- ["There Is Not Enough Information": On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Making](https://mlsanity.com/paper/2205.05758/) (2022)
- [A Critical Survey on Fairness Benefits of Explainable AI](https://mlsanity.com/paper/2310.13007/) (2023)
- [Perceptions of Fairness and Trustworthiness Based on Explanations in Human vs. Automated Decision-Making](https://mlsanity.com/paper/2109.05792/) (2021)
- [The Bots of Persuasion: Examining How Conversational Agents' Linguistic Expressions of Personality Affect User Perceptions and Decisions](https://mlsanity.com/paper/2602.17185/) (2026)
- [A Human-Centric Perspective on Fairness and Transparency in Algorithmic Decision-Making](https://mlsanity.com/paper/2205.00033/) (2022)
- [Mapping the Potential of Explainable AI for Fairness Along the AI Lifecycle](https://mlsanity.com/paper/2404.18736/) (2024)
- [Explainable AI Reloaded: Challenging the XAI Status Quo in the Era of Large Language Models](https://mlsanity.com/paper/2408.05345/) (2024)

## Abstract (arXiv)

It is often argued that one goal of explaining automated decision systems (ADS) is to facilitate positive perceptions (e.g., fairness or trustworthiness) of users towards such systems. This viewpoint, however, makes the implicit assumption that a given ADS is fair and trustworthy, to begin with. If the ADS issues unfair outcomes, then one might expect that explanations regarding the system's workings will reveal its shortcomings and, hence, lead to a decrease in fairness perceptions. Consequently, we suggest that it is more meaningful to evaluate explanations against their effectiveness in enabling people to appropriately assess the quality (e.g., fairness) of an associated ADS. We argue that for an effective explanation, perceptions of fairness should increase if and only if the underlying ADS is fair. In this in-progress work, we introduce the desideratum of appropriate fairness perceptions, propose a novel study design for evaluating it, and outline next steps towards a comprehensive experiment.