A review of causality-based fairness machine learning

Abstract

With the wide application of machine learning driven automated decisions (e.g., education, loan approval, and hiring) in daily life, it is critical to address the problem of discriminatory behavior toward certain individuals or groups. Early studies focused on defining the correlation/association-based notions, such as statistical parity, equalized odds, etc. However, recent studies reflect that it is necessary to use causality to address the problem of fairness. This review provides an exhaustive overview of notions and methods for detecting and eliminating algorithmic discrimination from a causality perspective. The review begins by introducing the common causality-based definitions and measures for fairness. We then review causality-based fairness-enhancing methods from the perspective of pre-processing, in-processing and post-processing mechanisms, and conduct a comprehensive analysis of the advantages, disadvantages, and applicability of these mechanisms. In addition, this review also examines other domains where researchers have observed unfair outcomes and the ways they have tried to address them. There are still many challenges that hinder the practical application of causality-based fairness notions, specifically the difficulty of acquiring causal graphs and identifiability of causal effects. One of the main purposes of this review is to spark more researchers to tackle these challenges in the near future.

Muscle synergy analysis for gesture recognition based on sEMG images and Shapley value

“…Grad-CAM (gradient-weighted class activation mapping) can be used to address this issue, which is an interpretable method for feature visualization and input unit importance attribution in neural networks [13][14][15] . While previous studies have not investigated the interpretability of gesture recognition methods based on surface electromyographic signals using this technique, we present the first results of an interpretability analysis of gesture recognition based on CNN [16] . Our approach visualizes the crucial features of muscles and focuses on muscle conjugation analysis to reduce redundant calculations.…”