# Exposing Algorithmic Discrimination and Its Consequences in Modern Society: Insights from a Scoping Study

arXiv: https://arxiv.org/abs/2312.04832 · HTML: https://mlsanity.com/paper/2312.04832/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2023.

## Built on by (most cited first, 4 of 4)

- [Automated Harmfulness Testing for Code Large Language Models](https://mlsanity.com/paper/2503.16740/) (2025)
- [A Gray Literature Study on Fairness Requirements in AI-enabled Software Engineering](https://mlsanity.com/paper/2512.07990/) (2025)
- [Intersectional Fairness in Large Language Models](https://mlsanity.com/paper/2604.20677/) (2026)
- [Ethics Testing: Proactive Identification of Generative AI System Harms](https://mlsanity.com/paper/2604.22089/) (2026)

## 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