featuresfairness

Features fairness, also referred to as featuresfairness, is a concept in machine learning and data ethics that concerns the fairness properties of the features used by predictive models. It focuses on how features are collected, represented, preprocessed, and selected to minimize unfair influence on outcomes across different groups, addressing not only model predictions but the data and feature engineering steps that drive them.

Key dimensions include data collection fairness (ensuring data do not embed biased or sensitive information implicitly), representation fairness (avoiding feature encodings that proxy protected attributes), feature leakage prevention (limiting unwanted leakage of sensitive information into features), and feature selection fairness (choosing features in a way that does not disproportionately favor or disadvantage groups). The goal is to reduce biased pathways from data to predictions through the feature space.

Metrics and evaluation for features fairness are diverse. Analysts examine the distribution of features across protected groups for parity, measure the dependence between features and sensitive attributes, and assess how feature subsets influence disparate impact on outcomes. Given the indirect nature of feature bias, combining multiple indicators and domain knowledge is common.

Methods to promote features fairness include data preprocessing techniques such as reweighting, resampling, or transforming features to remove sensitive signal; representation learning approaches that aim to learn features invariant to protected attributes; and in-processing methods that penalize unfair feature influence during model training. Some approaches also emphasize transparency and auditing of feature pipelines to identify biased proxies.

Challenges include defining universally applicable fairness criteria for features, balancing fairness with predictive accuracy, and addressing evolving data and regulatory contexts. Features fairness remains an active area of research in responsible AI and data governance.