# FairSight: Visual Analytics for Fairness in Decision Making

arXiv: https://arxiv.org/abs/1908.00176 · HTML: https://mlsanity.com/paper/1908.00176/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2019.

## Built on by (most cited first, 30 of 59)

- [A survey on datasets for fairness-aware machine learning](https://mlsanity.com/paper/2110.00530/) (2021)
- [Robustness Gym: Unifying the NLP Evaluation Landscape](https://mlsanity.com/paper/2101.04840/) (2021)
- [The Spotlight: A General Method for Discovering Systematic Errors in Deep Learning Models](https://mlsanity.com/paper/2107.00758/) (2021)
- [Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits](https://mlsanity.com/paper/2205.06922/) (2022)
- [M2Lens: Visualizing and Explaining Multimodal Models for Sentiment Analysis](https://mlsanity.com/paper/2107.08264/) (2021)
- [The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations](https://mlsanity.com/paper/2212.11737/) (2022)
- [Discovering and Validating AI Errors With Crowdsourced Failure Reports](https://mlsanity.com/paper/2109.11690/) (2021)
- [Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics](https://mlsanity.com/paper/2202.08792/) (2022)
- [Visual Analytics For Machine Learning: A Data Perspective Survey](https://mlsanity.com/paper/2307.07712/) (2023)
- [A Sociotechnical View of Algorithmic Fairness](https://mlsanity.com/paper/2110.09253/) (2021)
- [De-biasing "bias" measurement](https://mlsanity.com/paper/2205.05770/) (2022)
- [Symphony: Composing Interactive Interfaces for Machine Learning](https://mlsanity.com/paper/2202.08946/) (2022)
- [D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Bias](https://mlsanity.com/paper/2208.05126/) (2022)
- [A Critical Survey on Fairness Benefits of Explainable AI](https://mlsanity.com/paper/2310.13007/) (2023)
- [Towards Involving End-users in Interactive Human-in-the-loop AI Fairness](https://mlsanity.com/paper/2204.10464/) (2022)
- [How to be fair? A study of label and selection bias](https://mlsanity.com/paper/2403.14282/) (2024)
- [Towards Responsible AI: A Design Space Exploration of Human-Centered Artificial Intelligence User Interfaces to Investigate Fairness](https://mlsanity.com/paper/2206.00474/) (2022)
- [WordBias: An Interactive Visual Tool for Discovering Intersectional Biases Encoded in Word Embeddings](https://mlsanity.com/paper/2103.03598/) (2021)
- [End-To-End Bias Mitigation: Removing Gender Bias in Deep Learning](https://mlsanity.com/paper/2104.02532/) (2021)
- [Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference](https://mlsanity.com/paper/2404.03085/) (2024)
- [Exploring Empty Spaces: Human-in-the-Loop Data Augmentation](https://mlsanity.com/paper/2410.01088/) (2024)
- [Finspector: A Human-Centered Visual Inspection Tool for Exploring and Comparing Biases among Foundation Models](https://mlsanity.com/paper/2305.16937/) (2023)
- [ASAP: Interpretable Analysis and Summarization of AI-generated Image Patterns at Scale](https://mlsanity.com/paper/2404.02990/) (2024)
- [EARN Fairness: Explaining, Asking, Reviewing, and Negotiating Artificial Intelligence Fairness Metrics Among Stakeholders](https://mlsanity.com/paper/2407.11442/) (2024)
- [Diagrammatization and Abduction to Improve AI Interpretability With Domain-Aligned Explanations for Medical Diagnosis](https://mlsanity.com/paper/2302.01241/) (2023)
- [BIASINSPECTOR: Detecting Bias in Structured Data through LLM Agents](https://mlsanity.com/paper/2504.04855/) (2025)
- [TimeTuner: Diagnosing Time Representations for Time-Series Forecasting with Counterfactual Explanations](https://mlsanity.com/paper/2307.09916/) (2023)
- [Human-in-the-loop Fairness: Integrating Stakeholder Feedback to Incorporate Fairness Perspectives in Responsible AI](https://mlsanity.com/paper/2312.08064/) (2023)
- [FairCompass: Operationalising Fairness in Machine Learning](https://mlsanity.com/paper/2312.16726/) (2023)
- [How Robust is your Fair Model? Exploring the Robustness of Diverse Fairness Strategies](https://mlsanity.com/paper/2207.04581/) (2022)

## Abstract (arXiv)

Data-driven decision making related to individuals has become increasingly pervasive, but the issue concerning the potential discrimination has been raised by recent studies. In response, researchers have made efforts to propose and implement fairness measures and algorithms, but those efforts have not been translated to the real-world practice of data-driven decision making. As such, there is still an urgent need to create a viable tool to facilitate fair decision making. We propose FairSight, a visual analytic system to address this need; it is designed to achieve different notions of fairness in ranking decisions through identifying the required actions -- understanding, measuring, diagnosing and mitigating biases -- that together lead to fairer decision making. Through a case study and user study, we demonstrate that the proposed visual analytic and diagnostic modules in the system are effective in understanding the fairness-aware decision pipeline and obtaining more fair outcomes.