Exposing Algorithmic Discrimination and Its Consequences in Modern Society: Insights from a Scoping Study
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Abstract (arXiv)
Algorithmic discrimination is a condition that arises when data-driven software unfairly treats users based on attributes like ethnicity, race, gender, sexual orientation, religion, age, disability, or other personal characteristics. Nowadays, as machine learning gains popularity, cases of algorithmic discrimination are increasingly being reported in several contexts. This study delves into various studies published over the years reporting algorithmic discrimination. We aim to support software engineering researchers and practitioners in addressing this issue by discussing key characteristics of the problem
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Dec 2023.
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Citing papers per year: 2025: 2; 2026: 2.
4 papers.
- 2025Automated Harmfulness Testing for Code Large Language Modelscited by 0
- 2025A Gray Literature Study on Fairness Requirements in AI-enabled Software Engineeringcited by 0
- 2026Intersectional Fairness in Large Language Modelscited by 0
- 2026Ethics Testing: Proactive Identification of Generative AI System Harmscited by 0
- 2026Ethics Testing: Proactive Identification of Generative AI System Harms23 Apr 2026
- 2026Intersectional Fairness in Large Language Models22 Apr 2026
- 2025A Gray Literature Study on Fairness Requirements in AI-enabled Software Engineering8 Dec 2025
- 2025Automated Harmfulness Testing for Code Large Language Models20 Mar 2025