# A Study on Fairness and Trust Perceptions in Automated Decision Making

arXiv: https://arxiv.org/abs/2103.04757 · HTML: https://mlsanity.com/paper/2103.04757/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 38 references that are in the map.
- First posted on arXiv 8 Mar 2021.

## Builds on (most cited first, 3 of 3)

- ['It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions](https://mlsanity.com/paper/1801.10408/) (2018)
- [Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning](https://mlsanity.com/paper/1902.04783/) (2019)
- [Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differences](https://mlsanity.com/paper/2001.09604/) (2020)

## Built on by (most cited first, 7 of 7)

- [Fairness Perceptions of Algorithmic Decision-Making: A Systematic Review of the Empirical Literature](https://mlsanity.com/paper/2103.12016/) (2021)
- [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)
- [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)
- [Perceptions of Fairness and Trustworthiness Based on Explanations in Human vs. Automated Decision-Making](https://mlsanity.com/paper/2109.05792/) (2021)
- [Intelligent Decision Assistance Versus Automated Decision-Making: Enhancing Knowledge Work Through Explainable Artificial Intelligence](https://mlsanity.com/paper/2109.13827/) (2021)
- [A Human-Centric Perspective on Fairness and Transparency in Algorithmic Decision-Making](https://mlsanity.com/paper/2205.00033/) (2022)

## Abstract (arXiv)

Automated decision systems are increasingly used for consequential decision making -- for a variety of reasons. These systems often rely on sophisticated yet opaque models, which do not (or hardly) allow for understanding how or why a given decision was arrived at. This is not only problematic from a legal perspective, but non-transparent systems are also prone to yield undesirable (e.g., unfair) outcomes because their sanity is difficult to assess and calibrate in the first place. In this work, we conduct a study to evaluate different attempts of explaining such systems with respect to their effect on people's perceptions of fairness and trustworthiness towards the underlying mechanisms. A pilot study revealed surprising qualitative insights as well as preliminary significant effects, which will have to be verified, extended and thoroughly discussed in the larger main study.