# Perceptions of Discriminatory Decisions of Artificial Intelligence: Unpacking the Role of Individual Characteristics

arXiv: https://arxiv.org/abs/2410.13250 · HTML: https://mlsanity.com/paper/2410.13250/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 76 references that are in the map.
- First posted on arXiv 17 Oct 2024.

## Builds on (most cited first, 10 of 10)

- ['It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions](https://mlsanity.com/paper/1801.10408/) (2018)
- [Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making](https://mlsanity.com/paper/1802.01029/) (2018)
- [Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Making](https://mlsanity.com/paper/2101.05303/) (2021)
- [Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differences](https://mlsanity.com/paper/2001.09604/) (2020)
- [Trust in AI and Its Role in the Acceptance of AI Technologies](https://mlsanity.com/paper/2203.12687/) (2022)
- [Fairness Perceptions of Algorithmic Decision-Making: A Systematic Review of the Empirical Literature](https://mlsanity.com/paper/2103.12016/) (2021)
- [A Sociotechnical View of Algorithmic Fairness](https://mlsanity.com/paper/2110.09253/) (2021)
- [Auditing for Discrimination in Algorithms Delivering Job Ads](https://mlsanity.com/paper/2104.04502/) (2021)
- [AI-Ethics by Design. Evaluating Public Perception on the Importance of Ethical Design Principles of AI](https://mlsanity.com/paper/2106.00326/) (2021)
- [Detecting race and gender bias in visual representation of AI on web search engines](https://mlsanity.com/paper/2106.14072/) (2021)

## Abstract (arXiv)

This study investigates how personal differences (digital self-efficacy, technical knowledge, belief in equality, political ideology) and demographic factors (age, education, and income) are associated with perceptions of artificial intelligence (AI) outcomes exhibiting gender and racial bias and with general attitudes towards AI. Analyses of a large-scale experiment dataset (N = 1,206) indicate that digital self-efficacy and technical knowledge are positively associated with attitudes toward AI, while liberal ideologies are negatively associated with outcome trust, higher negative emotion, and greater skepticism. Furthermore, age and income are closely connected to cognitive gaps in understanding discriminatory AI outcomes. These findings highlight the importance of promoting digital literacy skills and enhancing digital self-efficacy to maintain trust in AI and beliefs in AI usefulness and safety. The findings also suggest that the disparities in understanding problematic AI outcomes may be aligned with economic inequalities and generational gaps in society. Overall, this study sheds light on the socio-technological system in which complex interactions occur between social hierarchies, divisions, and machines that reflect and exacerbate the disparities.