# Perceptions of Fairness and Trustworthiness Based on Explanations in Human vs. Automated Decision-Making

arXiv: https://arxiv.org/abs/2109.05792 · HTML: https://mlsanity.com/paper/2109.05792/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 57 references that are in the map.
- First posted on arXiv 13 Sep 2021.

## Builds on (most cited first, 7 of 7)

- ['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)
- [Auditing for Discrimination in Algorithms Delivering Job Ads](https://mlsanity.com/paper/2104.04502/) (2021)
- [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)
- [A Study on Fairness and Trust Perceptions in Automated Decision Making](https://mlsanity.com/paper/2103.04757/) (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)

## Built on by (most cited first, 3 of 3)

- [Should I Follow AI-based Advice? Measuring Appropriate Reliance in Human-AI Decision-Making](https://mlsanity.com/paper/2204.06916/) (2022)
- ["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 Human-Centric Perspective on Fairness and Transparency in Algorithmic Decision-Making](https://mlsanity.com/paper/2205.00033/) (2022)

## Abstract (arXiv)

Automated decision systems (ADS) have become ubiquitous in many high-stakes domains. Those systems typically involve sophisticated yet opaque artificial intelligence (AI) techniques that seldom allow for full comprehension of their inner workings, particularly for affected individuals. As a result, ADS are prone to deficient oversight and calibration, which can lead to undesirable (e.g., unfair) outcomes. In this work, we conduct an online study with 200 participants to examine people's perceptions of fairness and trustworthiness towards ADS in comparison to a scenario where a human instead of an ADS makes a high-stakes decision -- and we provide thorough identical explanations regarding decisions in both cases. Surprisingly, we find that people perceive ADS as fairer than human decision-makers. Our analyses also suggest that people's AI literacy affects their perceptions, indicating that people with higher AI literacy favor ADS more strongly over human decision-makers, whereas low-AI-literacy people exhibit no significant differences in their perceptions.