# D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Bias

arXiv: https://arxiv.org/abs/2208.05126 · HTML: https://mlsanity.com/paper/2208.05126/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 61 references that are in the map.
- First posted on arXiv 10 Aug 2022.

## Builds on (most cited first, 24 of 24)

- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias](https://mlsanity.com/paper/1810.01943/) (2018)
- [Fairness in Machine Learning](https://mlsanity.com/paper/2012.15816/) (2020)
- [Path-Specific Counterfactual Fairness](https://mlsanity.com/paper/1802.08139/) (2018)
- [FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning](https://mlsanity.com/paper/1904.05419/) (2019)
- [Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AI](https://mlsanity.com/paper/2005.05906/) (2020)
- [Bias Mitigation Post-processing for Individual and Group Fairness](https://mlsanity.com/paper/1812.06135/) (2018)
- [A causal framework for discovering and removing direct and indirect discrimination](https://mlsanity.com/paper/1611.07509/) (2016)
- [FairSight: Visual Analytics for Fairness in Decision Making](https://mlsanity.com/paper/1908.00176/) (2019)
- [Causal Discovery Toolbox: Uncover causal relationships in Python](https://mlsanity.com/paper/1903.02278/) (2019)
- [Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems](https://mlsanity.com/paper/2001.06509/) (2020)
- [TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networks](https://mlsanity.com/paper/2109.00666/) (2021)
- [Penalizing Unfairness in Binary Classification](https://mlsanity.com/paper/1707.00044/) (2017)
- [A Causal Bayesian Networks Viewpoint on Fairness](https://mlsanity.com/paper/1907.06430/) (2019)
- [On Discrimination Discovery and Removal in Ranked Data using Causal Graph](https://mlsanity.com/paper/1803.01901/) (2018)
- [Visual Analysis of Discrimination in Machine Learning](https://mlsanity.com/paper/2007.15182/) (2020)
- [Optimized Data Pre-Processing for Discrimination Prevention](https://mlsanity.com/paper/1704.03354/) (2017)
- [cuPC: CUDA-based Parallel PC Algorithm for Causal Structure Learning on GPU](https://mlsanity.com/paper/1812.08491/) (2018)
- [Outcome-Explorer: A Causality Guided Interactive Visual Interface for Interpretable Algorithmic Decision Making](https://mlsanity.com/paper/2101.00633/) (2021)
- [A Visual Analytics Approach for Exploratory Causal Analysis: Exploration, Validation, and Applications](https://mlsanity.com/paper/2009.02458/) (2020)
- [Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets](https://mlsanity.com/paper/1611.03227/) (2016)
- [WordBias: An Interactive Visual Tool for Discovering Intersectional Biases Encoded in Word Embeddings](https://mlsanity.com/paper/2103.03598/) (2021)
- [Cascaded Debiasing: Studying the Cumulative Effect of Multiple Fairness-Enhancing Interventions](https://mlsanity.com/paper/2202.03734/) (2022)

## Built on by (most cited first, 18 of 18)

- [Visual Analytics For Machine Learning: A Data Perspective Survey](https://mlsanity.com/paper/2307.07712/) (2023)
- [From Google Gemini to OpenAI Q* (Q-Star): A Survey of Reshaping the Generative Artificial Intelligence (AI) Research Landscape](https://mlsanity.com/paper/2312.10868/) (2023)
- [How to be fair? A study of label and selection bias](https://mlsanity.com/paper/2403.14282/) (2024)
- [An Explainable AI Approach to Large Language Model Assisted Causal Model Auditing and Development](https://mlsanity.com/paper/2312.16211/) (2023)
- [ASAP: Interpretable Analysis and Summarization of AI-generated Image Patterns at Scale](https://mlsanity.com/paper/2404.02990/) (2024)
- [Visual Analysis of Multi-outcome Causal Graphs](https://mlsanity.com/paper/2408.02679/) (2024)
- [Bias in the Loop: How Humans Evaluate AI-Generated Suggestions](https://mlsanity.com/paper/2509.08514/) (2025)
- [Comprehensive Validation on Reweighting Samples for Bias Mitigation via AIF360](https://mlsanity.com/paper/2312.12560/) (2023)
- [Explainable Interface for Human-Autonomy Teaming: A Survey](https://mlsanity.com/paper/2405.02583/) (2024)
- [Dataopsy: Scalable and Fluid Visual Exploration using Aggregate Query Sculpting](https://mlsanity.com/paper/2308.02764/) (2023)
- [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)
- [Situated Ground Truths: Enhancing Bias-Aware AI by Situating Data Labels with SituAnnotate](https://mlsanity.com/paper/2406.07583/) (2024)
- [Revisiting Technical Bias Mitigation Strategies](https://mlsanity.com/paper/2410.17433/) (2024)
- [Using AI Alignment Theory to understand the potential pitfalls of regulatory frameworks](https://mlsanity.com/paper/2410.19749/) (2024)
- [Towards Objective and Unbiased Decision Assessments with LLM-Enhanced Hierarchical Attention Networks](https://mlsanity.com/paper/2411.08504/) (2024)
- [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)
- [Human-Guided Causal Knowledge Injection for Virtual Cells](https://mlsanity.com/paper/2608.08430/) (2026)

## Abstract (arXiv)

With the rise of AI, algorithms have become better at learning underlying patterns from the training data including ingrained social biases based on gender, race, etc. Deployment of such algorithms to domains such as hiring, healthcare, law enforcement, etc. has raised serious concerns about fairness, accountability, trust and interpretability in machine learning algorithms. To alleviate this problem, we propose D-BIAS, a visual interactive tool that embodies human-in-the-loop AI approach for auditing and mitigating social biases from tabular datasets. It uses a graphical causal model to represent causal relationships among different features in the dataset and as a medium to inject domain knowledge. A user can detect the presence of bias against a group, say females, or a subgroup, say black females, by identifying unfair causal relationships in the causal network and using an array of fairness metrics. Thereafter, the user can mitigate bias by acting on the unfair causal edges. For each interaction, say weakening/deleting a biased causal edge, the system uses a novel method to simulate a new (debiased) dataset based on the current causal model. Users can visually assess the impact of their interactions on different fairness metrics, utility metrics, data distortion, and the underlying data distribution. Once satisfied, they can download the debiased dataset and use it for any downstream application for fairer predictions. We evaluate D-BIAS by conducting experiments on 3 datasets and also a formal user study. We found that D-BIAS helps reduce bias significantly compared to the baseline debiasing approach across different fairness metrics while incurring little data distortion and a small loss in utility. Moreover, our human-in-the-loop based approach significantly outperforms an automated approach on trust, interpretability and accountability.