# Bias and Discrimination in AI: a cross-disciplinary perspective

arXiv: https://arxiv.org/abs/2008.07309 · HTML: https://mlsanity.com/paper/2008.07309/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2020.

## Built on by (most cited first, 24 of 24)

- [When Search Engine Services meet Large Language Models: Visions and Challenges](https://mlsanity.com/paper/2407.00128/) (2024)
- [Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models](https://mlsanity.com/paper/2310.00566/) (2023)
- [MindShift: Leveraging Large Language Models for Mental-States-Based Problematic Smartphone Use Intervention](https://mlsanity.com/paper/2309.16639/) (2023)
- [Fairness Score and Process Standardization: Framework for Fairness Certification in Artificial Intelligence Systems](https://mlsanity.com/paper/2201.06952/) (2022)
- [Neuro-Symbolic AI for Military Applications](https://mlsanity.com/paper/2408.09224/) (2024)
- [Prototypical Self-Explainable Models Without Re-training](https://mlsanity.com/paper/2312.07822/) (2023)
- [A Two-Stage Algorithm for Cost-Efficient Multi-instance Counterfactual Explanations](https://mlsanity.com/paper/2403.01221/) (2024)
- [Fairness in Forecasting of Observations of Linear Dynamical Systems](https://mlsanity.com/paper/2209.05274/) (2022)
- [Fair Models in Credit: Intersectional Discrimination and the Amplification of Inequity](https://mlsanity.com/paper/2308.02680/) (2023)
- [Unpacking Human-AI Interaction in Safety-Critical Industries: A Systematic Literature Review](https://mlsanity.com/paper/2310.03392/) (2023)
- [Assessing Gender Bias in Predictive Algorithms using eXplainable AI](https://mlsanity.com/paper/2203.10264/) (2022)
- [Broadening the perspective for sustainable AI: Comprehensive sustainability criteria and indicators for AI systems](https://mlsanity.com/paper/2306.13686/) (2023)
- [AI for All: Identifying AI incidents Related to Diversity and Inclusion](https://mlsanity.com/paper/2408.01438/) (2024)
- [When AI Fails, What Works? A Data-Driven Taxonomy of Real-World AI Risk Mitigation Strategies](https://mlsanity.com/paper/2603.04259/) (2026)
- [Software Doping Analysis for Human Oversight](https://mlsanity.com/paper/2308.06186/) (2023)
- [Beyond Automation: Socratic AI, Epistemic Agency, and the Implications of the Emergence of Orchestrated Multi-Agent Learning Architectures](https://mlsanity.com/paper/2508.05116/) (2025)
- [Integrating Neurosymbolic AI in Advanced Air Mobility: A Comprehensive Survey](https://mlsanity.com/paper/2508.07163/) (2025)
- [On Explaining Proxy Discrimination and Unfairness in Individual Decisions Made by AI Systems](https://mlsanity.com/paper/2509.25662/) (2025)
- [Neural Contrastive Clustering: Fully Unsupervised Bias Reduction for Sentiment Classification](https://mlsanity.com/paper/2204.10467/) (2022)
- [Unleashing the Power of AI. A Systematic Review of Cutting-Edge Techniques in AI-Enhanced Scientometrics, Webometrics, and Bibliometrics](https://mlsanity.com/paper/2403.18838/) (2024)
- [LLM-Guided Synthetic Augmentation (LGSA) for Mitigating Bias in AI Systems](https://mlsanity.com/paper/2510.13202/) (2025)
- [Textual Data Bias Detection and Mitigation -- An Extensible Pipeline with Experimental Evaluation](https://mlsanity.com/paper/2512.10734/) (2025)
- [A Blended Likelihood Approach for Achieving Fairness Using Naive Bayes](https://mlsanity.com/paper/2605.25228/) (2026)
- [Security and Privacy in Agentic AI: Grand Challenges and Future Directions](https://mlsanity.com/paper/2607.06608/) (2026)

## Abstract (arXiv)

With the widespread and pervasive use of Artificial Intelligence (AI) for automated decision-making systems, AI bias is becoming more apparent and problematic. One of its negative consequences is discrimination: the unfair, or unequal treatment of individuals based on certain characteristics. However, the relationship between bias and discrimination is not always clear. In this paper, we survey relevant literature about bias and discrimination in AI from an interdisciplinary perspective that embeds technical, legal, social and ethical dimensions. We show that finding solutions to bias and discrimination in AI requires robust cross-disciplinary collaborations.