Referee-Meta-Learning for Fast Adaptation of Locational Fairness
Weiye Chen Yiqun Xie Xiaowei Jia Erhu He Han Bao Bang An Xun Zhou
When dealing with data from distinct locations, machine learning algorithms
tend to demonstrate an implicit preference of some locations over the others,
which constitutes biases that sabotage the spatial fairness of the algorithm.
This unfairness can easily introduce biases in subsequent decision-making given
broad adoptions of learning-based solutions in practice. However, locational
biases in AI are largely understudied. To mitigate biases over locations, we
propose a locational meta-referee (Meta-Ref) to oversee the few-shot
meta-training and meta-testing of a deep neural network. Meta-Ref dynamically
adjusts the learning rates for training samples of given locations to advocate
a fair performance across locations, through an explicit consideration of
locational biases and the characteristics of input data. We present a
three-phase training framework to learn both a meta-learning-based predictor
and an integrated Meta-Ref that governs the fairness of the model. Once trained
with a distribution of spatial tasks, Meta-Ref is applied to samples from new
spatial tasks (i.e., regions outside the training area) to promote fairness
during the fine-tune step. We carried out experiments with two case studies on
crop monitoring and transportation safety, which show Meta-Ref can improve
locational fairness while keeping the overall prediction quality at a similar
level.