Fairness-Aware Streaming Feature Selection with Causal Graphs

TL;DR

This paper introduces SFCF, a causal graph-based streaming feature selection method that balances accuracy and fairness by removing biased features, demonstrating superior efficiency and fairness in multiple datasets.

Contribution

The paper proposes a novel causal graph approach for streaming feature selection that effectively mitigates bias while maintaining predictive accuracy.

Findings

SFCF outperforms six rival models in efficiency and sparsity.

SFCF achieves better equalized odds in predictive models.

The method effectively removes causally biased features from streaming data.

Abstract

Its crux lies in the optimization of a tradeoff between accuracy and fairness of resultant models on the selected feature subset. The technical challenge of our setting is twofold: 1) streaming feature inputs, such that an informative feature may become obsolete or redundant for prediction if its information has been covered by other similar features that arrived prior to it, and 2) non-associational feature correlation, such that bias may be leaked from those seemingly admissible, non-protected features. To overcome this, we propose Streaming Feature Selection with Causal Fairness (SFCF) that builds two causal graphs egocentric to prediction label and protected feature, respectively, striving to model the complex correlation structure among streaming features, labels, and protected information. As such, bias can be eradicated from predictive modeling by removing those features being causally correlated with the protected feature yet independent to the labels. We theorize that the originally redundant features for prediction can later become admissible, when the learning accuracy is compromised by the large number of removed features (non-protected but can be used to reconstruct bias information). We benchmark SFCF\ on five datasets widely used in streaming feature research, and the results substantiate its performance superiority over six rival models in terms of efficiency and sparsity of feature selection and equalized odds of the resultant predictive models.