Towards Assumption-free Bias Mitigation
Chia-Yuan Chang Yu-Neng Chuang Kwei-Herng Lai Xiaotian Han Xia Hu Na Zou
Despite the impressive prediction ability, machine learning models show
discrimination towards certain demographics and suffer from unfair prediction
behaviors. To alleviate the discrimination, extensive studies focus on
eliminating the unequal distribution of sensitive attributes via multiple
approaches. However, due to privacy concerns, sensitive attributes are often
either unavailable or missing in real-world scenarios. Therefore, several
existing works alleviate the bias without sensitive attributes. Those studies
face challenges, either in inaccurate predictions of sensitive attributes or
the need to mitigate unequal distribution of manually defined non-sensitive
attributes related to bias. The latter requires strong assumptions about the
correlation between sensitive and non-sensitive attributes. As data
distribution and task goals vary, the strong assumption on non-sensitive
attributes may not be valid and require domain expertise. In this work, we
propose an assumption-free framework to detect the related attributes
automatically by modeling feature interaction for bias mitigation. The proposed
framework aims to mitigate the unfair impact of identified biased feature
interactions. Experimental results on four real-world datasets demonstrate that
our proposed framework can significantly alleviate unfair prediction behaviors
by considering biased feature interactions.