D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Bias
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D-BIAS：基于因果模型的人机协同算法偏差治理系统
- 研究问题
- 如何让用户通过领域知识和图形因果模型识别表格数据中的群体偏差，并在公平性、预测效用及数据失真之间评估修正操作？
- 方法与思路
- 作者构建交互式可视化工具，让用户调整被判为不公平的因果边，再依据当前因果模型模拟新数据，并比较多种指标和用户体验。
- 材料陈述的贡献
- 摘要称在三个数据集实验和用户研究中，系统相对基线降低多项公平性偏差，数据失真及效用损失较小，并改善信任等评价。
- 阅读关注点
- 应核对因果图来源、不公平边的判定依据与数据模拟机制，检查公平指标间冲突，以及基线、效用损失和用户研究的设计。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要未说明图结构验证与数值结果，实验改善不能保证任意下游应用更公平。
原文摘要
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.