A Survey on Bias in Visual Datasets

Simone Fabbrizzi, Symeon Papadopoulos, Eirini Ntoutsi, Ioannis Kompatsiaris

Computer Vision (CV) has achieved remarkable results, outperforming humans in several tasks. Nonetheless, it may result in major discrimination if not dealt with proper care. CV systems highly depend on the data they are fed with and can learn and amplify biases within such data. Thus, both the problems of understanding and discovering biases are of utmost importance. Yet, to date there is no comprehensive survey on bias in visual datasets. To this end, this work aims to: i) describe the biases that can affect visual datasets; ii) review the literature on methods for bias discovery and quantification in visual datasets; iii) discuss existing attempts to collect bias-aware visual datasets. A key conclusion of our study is that the problem of bias discovery and quantification in visual datasets is still open and there is room for improvement in terms of both methods and the range of biases that can be addressed; moreover, there is no such thing as a bias-free dataset, so scientists and practitioners must become aware of the biases in their datasets and make them explicit. To this end, we propose a checklist that can be used to spot different types of bias during visual dataset collection.

Knowledge Graph

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