%0 Journal Article %1 Fabris2022 %A Fabris, Alessandro %A Messina, Stefano %A Silvello, Gianmaria %A Susto, Gian Antonio %D 2022 %J Data Mining and Knowledge Discovery %K algorithmic fairness learning machine themis toread %N 6 %P 2074--2152 %R 10.1007/s10618-022-00854-z %T Algorithmic fairness datasets: the story so far %U https://doi.org/10.1007/s10618-022-00854-z %V 36 %X Data-driven algorithms are studied and deployed 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 and equitable algorithm design hinges on data, which can be appropriately used only if adequately documented. Unfortunately, the algorithmic fairness community, as a whole, suffers from a collective data documentation debt caused by a lack of information on specific resources (opacity) and scatteredness of available information (sparsity). In this work, we target this data documentation debt by surveying over two hundred datasets employed in algorithmic fairness research, and producing standardized and searchable documentation for each of them. Moreover we rigorously identify the three most popular fairness datasets, namely Adult, COMPAS, and German Credit, for which we compile in-depth documentation. This unifying documentation effort supports multiple contributions. Firstly, we summarize the merits and limitations of Adult, COMPAS, and German Credit, adding to and unifying recent scholarship, calling into question their suitability as general-purpose fairness benchmarks. Secondly, we document hundreds of available alternatives, annotating their domain and supported fairness tasks, along with additional properties of interest for fairness practitioners and researchers, including their format, cardinality, and the sensitive attributes they encode. We summarize this information, zooming in on the tasks, domains, and roles of these resources. Finally, we analyze these datasets from the perspective of five important data curation topics: anonymization, consent, inclusivity, labeling of sensitive attributes, and transparency. We discuss different approaches and levels of attention to these topics, making them tangible, and distill them into a set of best practices for the curation of novel resources.

@article{Fabris2022, abstract = {Data-driven algorithms are studied and deployed 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 and equitable algorithm design hinges on data, which can be appropriately used only if adequately documented. Unfortunately, the algorithmic fairness community, as a whole, suffers from a collective data documentation debt caused by a lack of information on specific resources (opacity) and scatteredness of available information (sparsity). In this work, we target this data documentation debt by surveying over two hundred datasets employed in algorithmic fairness research, and producing standardized and searchable documentation for each of them. Moreover we rigorously identify the three most popular fairness datasets, namely Adult, COMPAS, and German Credit, for which we compile in-depth documentation. This unifying documentation effort supports multiple contributions. Firstly, we summarize the merits and limitations of Adult, COMPAS, and German Credit, adding to and unifying recent scholarship, calling into question their suitability as general-purpose fairness benchmarks. Secondly, we document hundreds of available alternatives, annotating their domain and supported fairness tasks, along with additional properties of interest for fairness practitioners and researchers, including their format, cardinality, and the sensitive attributes they encode. We summarize this information, zooming in on the tasks, domains, and roles of these resources. Finally, we analyze these datasets from the perspective of five important data curation topics: anonymization, consent, inclusivity, labeling of sensitive attributes, and transparency. We discuss different approaches and levels of attention to these topics, making them tangible, and distill them into a set of best practices for the curation of novel resources.}, added-at = {2022-11-21T09:32:49.000+0100}, author = {Fabris, Alessandro and Messina, Stefano and Silvello, Gianmaria and Susto, Gian Antonio}, biburl = {https://www.bibsonomy.org/bibtex/27a740657cc7a8ada874071f1c440eb75/becker}, day = 01, description = {Algorithmic fairness datasets: the story so far | SpringerLink}, doi = {10.1007/s10618-022-00854-z}, interhash = {411dd351cb551140a98d38dd3e02779c}, intrahash = {7a740657cc7a8ada874071f1c440eb75}, issn = {1573-756X}, journal = {Data Mining and Knowledge Discovery}, keywords = {algorithmic fairness learning machine themis toread}, month = nov, number = 6, pages = {2074--2152}, timestamp = {2022-11-21T09:32:49.000+0100}, title = {Algorithmic fairness datasets: the story so far}, url = {https://doi.org/10.1007/s10618-022-00854-z}, volume = 36, year = 2022 }