mlsanityData to 7 Oct 2026

Bias and Discrimination in AI: a cross-disciplinary perspective

arXiv 2008.07309 ↗First posted 11 Aug 2020cs.CY cs.LGcited by 24

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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.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Aug 2020.

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Citing papers per year: 2022: 4; 2023: 7; 2024: 5; 2025: 5; 2026: 3.

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  1. 2024When Search Engine Services meet Large Language Models: Visions and Challengescited by 59
  2. 2023Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Modelscited by 20
  3. 2023MindShift: Leveraging Large Language Models for Mental-States-Based Problematic Smartphone Use Interventioncited by 16
  4. 2022Fairness Score and Process Standardization: Framework for Fairness Certification in Artificial Intelligence Systemscited by 14
  5. 2024Neuro-Symbolic AI for Military Applicationscited by 7
  6. 2023Prototypical Self-Explainable Models Without Re-trainingcited by 6
  7. 2024A Two-Stage Algorithm for Cost-Efficient Multi-instance Counterfactual Explanationscited by 5
  8. 2022Fairness in Forecasting of Observations of Linear Dynamical Systemscited by 4
  9. 2023Fair Models in Credit: Intersectional Discrimination and the Amplification of Inequitycited by 3
  10. 2023Unpacking Human-AI Interaction in Safety-Critical Industries: A Systematic Literature Reviewcited by 3
  11. 2022Assessing Gender Bias in Predictive Algorithms using eXplainable AIcited by 2
  12. 2023Broadening the perspective for sustainable AI: Comprehensive sustainability criteria and indicators for AI systemscited by 2
12 more
  1. 2024AI for All: Identifying AI incidents Related to Diversity and Inclusioncited by 2
  2. 2023Software Doping Analysis for Human Oversightcited by 1
  3. 2025Beyond Automation: Socratic AI, Epistemic Agency, and the Implications of the Emergence of Orchestrated Multi-Agent Learning Architecturescited by 1
  4. 2025Integrating Neurosymbolic AI in Advanced Air Mobility: A Comprehensive Surveycited by 1
  5. 2025On Explaining Proxy Discrimination and Unfairness in Individual Decisions Made by AI Systemscited by 1
  6. 2026When AI Fails, What Works? A Data-Driven Taxonomy of Real-World AI Risk Mitigation Strategiescited by 1
  7. 2022Neural Contrastive Clustering: Fully Unsupervised Bias Reduction for Sentiment Classificationcited by 0
  8. 2024Unleashing the Power of AI. A Systematic Review of Cutting-Edge Techniques in AI-Enhanced Scientometrics, Webometrics, and Bibliometricscited by 0
  9. 2025LLM-Guided Synthetic Augmentation (LGSA) for Mitigating Bias in AI Systemscited by 0
  10. 2025Textual Data Bias Detection and Mitigation -- An Extensible Pipeline with Experimental Evaluationcited by 0
  11. 2026A Blended Likelihood Approach for Achieving Fairness Using Naive Bayescited by 0
  12. 2026Security and Privacy in Agentic AI: Grand Challenges and Future Directionscited by 0