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Despite being responsible for state-of-the-art results in several computervision and natural language processing tasks, neural networks have faced harshcriticism due to some of their current shortcomings. One of them is that neuralnetworks are correlation machines prone to model biases within the data insteadof focusing on actual useful causal relationships. This problem is particularlyserious in application domains affected by aspects such as race, gender, andage. To prevent models from incurring on unfair decision-making, the AIcommunity has concentrated efforts in correcting algorithmic biases, givingrise to the research area now widely known as fairness in AI. In this surveypaper, we provide an in-depth overview of the main debiasing methods forfairness-aware neural networks in the context of vision and language research.We propose a novel taxonomy to better organize the literature on debiasingmethods for fairness, and we discuss the current challenges, trends, andimportant future work directions for the interested researcher andpractitioner.

Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Survey