# Visual Analysis of Discrimination in Machine Learning

arXiv: https://arxiv.org/abs/2007.15182 · HTML: https://mlsanity.com/paper/2007.15182/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2020.

## Built on by (most cited first, 15 of 15)

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- [Preventing Discriminatory Decision-making in Evolving Data Streams](https://mlsanity.com/paper/2302.08017/) (2023)
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- [Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligence](https://mlsanity.com/paper/2301.04020/) (2022)
- [Visual Analysis of Neural Architecture Spaces for Summarizing Design Principles](https://mlsanity.com/paper/2208.09665/) (2022)
- [Human-in-the-loop Fairness: Integrating Stakeholder Feedback to Incorporate Fairness Perspectives in Responsible AI](https://mlsanity.com/paper/2312.08064/) (2023)
- [Classifying Objects in 3D Point Clouds Using Recurrent Neural Network: A GRU LSTM Hybrid Approach](https://mlsanity.com/paper/2403.05950/) (2024)
- [Interactive Counterfactual Exploration of Algorithmic Harms in Recommender Systems](https://mlsanity.com/paper/2409.06916/) (2024)
- [FairPlay: A Collaborative Approach to Mitigate Bias in Datasets for Improved AI Fairness](https://mlsanity.com/paper/2504.16255/) (2025)
- [Visual Identification of Problematic Bias in Large Label Spaces](https://mlsanity.com/paper/2201.06386/) (2022)
- [LINGO : Visually Debiasing Natural Language Instructions to Support Task Diversity](https://mlsanity.com/paper/2304.06184/) (2023)
- [VAR: Visual Analysis for Rashomon Set of Machine Learning Models' Performance](https://mlsanity.com/paper/2507.22556/) (2025)
- [Your Model Is Unfair, Are You Even Aware? Inverse Relationship Between Comprehension and Trust in Explainability Visualizations of Biased ML Models](https://mlsanity.com/paper/2508.00140/) (2025)
- [Towards Explainable Quantum AI: Informing the Encoder Selection of Quantum Neural Networks via Visualization](https://mlsanity.com/paper/2512.14181/) (2025)

## Abstract (arXiv)

The growing use of automated decision-making in critical applications, such as crime prediction and college admission, has raised questions about fairness in machine learning. How can we decide whether different treatments are reasonable or discriminatory? In this paper, we investigate discrimination in machine learning from a visual analytics perspective and propose an interactive visualization tool, DiscriLens, to support a more comprehensive analysis. To reveal detailed information on algorithmic discrimination, DiscriLens identifies a collection of potentially discriminatory itemsets based on causal modeling and classification rules mining. By combining an extended Euler diagram with a matrix-based visualization, we develop a novel set visualization to facilitate the exploration and interpretation of discriminatory itemsets. A user study shows that users can interpret the visually encoded information in DiscriLens quickly and accurately. Use cases demonstrate that DiscriLens provides informative guidance in understanding and reducing algorithmic discrimination.