We study fairness in supervised few-shot meta-learning models that are
sensitive to discrimination (or bias) in historical data. A machine learning
model trained based on biased data tends to make unfair predictions for users
from minority groups. Although this problem has been studied before, existing
methods mainly aim to detect and control the dependency effect of the protected
variables (e.g. race, gender) on target prediction based on a large amount of
training data. These approaches carry two major drawbacks that (1) lacking
showing a global cause-effect visualization for all variables; (2) lacking
generalization of both accuracy and fairness to unseen tasks. In this work, we
first discover discrimination from data using a causal Bayesian knowledge graph
which not only demonstrates the dependency of the protected variable on target
but also indicates causal effects between all variables. Next, we develop a
novel algorithm based on risk difference in order to quantify the
discriminatory influence for each protected variable in the graph. Furthermore,
to protect prediction from unfairness, a fast-adapted bias-control approach in
meta-learning is proposed, which efficiently mitigates statistical disparity
for each task and it thus ensures independence of protected attributes on
predictions based on biased and few-shot data samples. Distinct from existing
meta-learning models, group unfairness of tasks are efficiently reduced by
leveraging the mean difference between (un)protected groups for regression
problems. Through extensive experiments on both synthetic and real-world data
sets, we demonstrate that our proposed unfairness discovery and prevention
approaches efficiently detect discrimination and mitigate biases on model
output as well as generalize both accuracy and fairness to unseen tasks with a
limited amount of training samples.

本文通過創建因果貝葉斯知識圖，對機器學習模型中的歷史數據中現有的偏見進行了發現，提出了一種新的基於風險差的算法，以量化每個受保護變量在圖中的歧視影響力，以及一種快速適應的偏差控制方法，在元學習中高效地減少了組的不公平性，最終在合成和實際數據集上進行了廣泛實驗，證明了我們提出的不公平發現和防治方法可以高效地檢測偏見並減輕模型輸出上的偏見以及對訓練樣本量少的未見任務的精度和公平度的泛化。