Muffin: A Framework Toward Multi-Dimension AI Fairness by Uniting
  Off-the-Shelf Models
Yi Sheng Junhuan Yang Lei Yang Yiyu Shi Jingtongf Hu Weiwen Jiang
Model fairness (a.k.a., bias) has become one of the most critical problems in
a wide range of AI applications. An unfair model in autonomous driving may
cause a traffic accident if corner cases (e.g., extreme weather) cannot be
fairly regarded; or it will incur healthcare disparities if the AI model
misdiagnoses a certain group of people (e.g., brown and black skin). In recent
years, there have been emerging research works on addressing unfairness, and
they mainly focus on a single unfair attribute, like skin tone; however,
real-world data commonly have multiple attributes, among which unfairness can
exist in more than one attribute, called 'multi-dimensional fairness'. In this
paper, we first reveal a strong correlation between the different unfair
attributes, i.e., optimizing fairness on one attribute will lead to the
collapse of others. Then, we propose a novel Multi-Dimension Fairness
framework, namely Muffin, which includes an automatic tool to unite
off-the-shelf models to improve the fairness on multiple attributes
simultaneously. Case studies on dermatology datasets with two unfair attributes
show that the existing approach can achieve 21.05% fairness improvement on the
first attribute while it makes the second attribute unfair by 1.85%. On the
other hand, the proposed Muffin can unite multiple models to achieve
simultaneously 26.32% and 20.37% fairness improvement on both attributes;
meanwhile, it obtains 5.58% accuracy gain.