A survey on datasets for fairness-aware machine learning
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As decision-making increasingly relies on machine learning and (big) data, the issue of fairness in data-driven AI systems is receiving increasing attention from both research and industry. A large variety of fairness-aware machine learning solutions have been proposed which propose fairness-related interventions in the data, learning algorithms and/or model outputs. However, a vital part of proposing new approaches is evaluating them empirically on benchmark datasets that represent realistic and diverse settings. Therefore, in this paper, we overview real-world datasets used for fairness-aware machine learning. We focus on tabular data as the most common data representation for fairness-aware machine learning. We start our analysis by identifying relationships among the different attributes, particularly w.r.t. protected attributes and class attributes, using a Bayesian network. For a deeper understanding of bias and fairness in the datasets, we investigate the interesting relationships using exploratory analysis.
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