  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.
