  Existing facial analysis systems have been shown to yield biased results
against certain demographic subgroups. Due to its impact on society, it has
become imperative to ensure that these systems do not discriminate based on
gender, identity, or skin tone of individuals. This has led to research in the
identification and mitigation of bias in AI systems. In this paper, we
encapsulate bias detection/estimation and mitigation algorithms for facial
analysis. Our main contributions include a systematic review of algorithms
proposed for understanding bias, along with a taxonomy and extensive overview
of existing bias mitigation algorithms. We also discuss open challenges in the
field of biased facial analysis.
