Artificial-intelligence-assisted decision-making is appearing increasingly more frequently in our daily lives; however, it has been shown that biased data can cause unfairness in decision-making. In light of this, computational techniques are needed to limit the inequities in algorithmic decision-making. In this letter, we present a framework to join fair feature selection and fair meta-learning to do few-shot classification, which contains three parts: (1) a pre-processing component acts as an intermediate bridge between fair genetic algorithm (FairGA) and fair few-shot (FairFS) to generate the feature pool; (2) the FairGA module considers the presence or absence of words as gene expression, and filters out key features by a fairness clustering genetic algorithm; (3) the FairFS part carries out the task of representation and fairness constraint classification. Meanwhile, we propose a combinatorial loss function to cope with fairness constraints and hard samples. Experiments show that the proposed method achieves strong competitive performance on three public benchmarks.
Keywords: fairness; feature selection; few-shot; genetic algorithm; meta-learning.