AIM: Attributing, Interpreting, Mitigating Data Unfairness
Data collected in the real world often encapsulates historical discrimination against disadvantaged groups and individuals. Existing fair machine learning (FairML) research has predominantly focused on mitigating discriminative bias in the model prediction, with far less effort dedicated towards exploring how to trace biases present in the data, despite its importance for the transparency and interpretability of FairML. To fill this gap, we investigate a novel research problem: discovering samples that reflect biases/prejudices from the training data. Grounding on the existing fairness notions, we lay out a sample bias criterion and propose practical algorithms for measuring and countering sample bias. The derived bias score provides intuitive sample-level attribution and explanation of historical bias in data. On this basis, we further design two FairML strategies via sample-bias-informed minimal data editing. They can mitigate both group and individual unfairness at the cost of minimal or zero predictive utility loss. Extensive experiments and analyses on multiple real-world datasets demonstrate the effectiveness of our methods in explaining and mitigating unfairness. Code is available at https://github.com/ZhiningLiu1998/AIM.
Code (1)
Tasks
FairnessSimilar Papers 제목 키워드 기반
Interpreting Unfairness in Graph Neural Networks via Training Node Attribution
Graph Neural Networks (GNNs) have emerged as the leading paradigm for solving graph analytical problems in various real-world applications. Nevertheless, GNNs could potentially render biased predictions towards certain d…
A Characteristic Function for Shapley-Value-Based Attribution of Anomaly Scores
In anomaly detection, the degree of irregularity is often summarized as a real-valued anomaly score. We address the problem of attributing such anomaly scores to input features for interpreting the results of anomaly det…
Anomaly DetectionSemi-supervised Anomaly DetectionSupervised Anomaly DetectionA Causal Linear Model to Quantify Edge Flow and Edge Unfairness for UnfairEdge Prioritization and Discrimination Removal
Law enforcement must prioritize sources of unfairness before mitigating their underlying unfairness, considering that they have limited resources. Unlike previous works that only make cautionary claims of discrimination …
Mitigating Unfairness via Evolutionary Multi-objective Ensemble Learning
In the literature of mitigating unfairness in machine learning, many fairness measures are designed to evaluate predictions of learning models and also utilised to guide the training of fair models. It has been theoretic…
Ensemble LearningFairnessTowards Faithful Neural Network Intrinsic Interpretation with Shapley Additive Self-Attribution
Self-interpreting neural networks have garnered significant interest in research. Existing works in this domain often (1) lack a solid theoretical foundation ensuring genuine interpretability or (2) compromise model expr…