2022

Algorithmic Fairness Datasets: the Story so Far

Fabris, Alessandro, Messina, Stefano, Silvello, Gianmaria et al.

Understand

Data-driven algorithms are studied in diverse domains to support critical decisions, directly impacting people's well-being.

As a result, a growing community of researchers has been investigating the equity of existing algorithms and proposing novel ones, advancing the understanding of risks and opportunities of automated decision-making for historically disadvantaged populations.
Progress in fair Machine Learning hinges on data, which can be appropriately used only if adequately documented.
Unfortunately, the algorithmic fairness community suffers from a collective data documentation debt caused by a lack of information on specific resources (opacity) and scatteredness of available information (sparsity).

Built on

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