This dataset presents the description and the references for the datasets used in the fairness literature. 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. For over 95% of the surveyed datasets, we identified at least one contact involved in the data curation process or familiar with the dataset, who received a preliminary version of the respective data brief and a request for corrections and additions. Data briefs are meant as short documentation providing essential information on datasets used in fairness research.The dataset is a supplement to the preprint paper available here: https://arxiv.o...
International audienceFairness of algorithms is the subject of a large body of literature, of guides...
Notwithstanding the widely held view that data generation and data curation processes are prominent ...
Algorithms can now identify patterns and correlations in the (big) datasets, and predict outcomes ba...
This dataset presents the description and the references for the datasets used in the fairness liter...
Data-driven algorithms are studied in diverse domains to support critical decisions, directly impact...
A growing community of researchers has been investigating the equity of algorithms, advancing the un...
Group fairness means that different groups have an equal probability of being predicted for one aspe...
In a world where the algorithm can control the lives of society, it is not surprising that specific ...
The increasing use of data-driven decision support systems in industry and governments is accompanie...
As decision-making increasingly relies on machine learning (ML) and (big) data, the issue of fairnes...
Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland A...
Over the last years, a wide spread of Machine Learning in increasingly more, especially sensitive ar...
Automated decision systems are increasingly used to take consequential decisions in problems such as...
Algorithmic fairness research is currently receiving significant attention, aiming to ensure that al...
The data science era is characterized by data-driven automated decision systems (ADS) enabling,...
International audienceFairness of algorithms is the subject of a large body of literature, of guides...
Notwithstanding the widely held view that data generation and data curation processes are prominent ...
Algorithms can now identify patterns and correlations in the (big) datasets, and predict outcomes ba...
This dataset presents the description and the references for the datasets used in the fairness liter...
Data-driven algorithms are studied in diverse domains to support critical decisions, directly impact...
A growing community of researchers has been investigating the equity of algorithms, advancing the un...
Group fairness means that different groups have an equal probability of being predicted for one aspe...
In a world where the algorithm can control the lives of society, it is not surprising that specific ...
The increasing use of data-driven decision support systems in industry and governments is accompanie...
As decision-making increasingly relies on machine learning (ML) and (big) data, the issue of fairnes...
Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland A...
Over the last years, a wide spread of Machine Learning in increasingly more, especially sensitive ar...
Automated decision systems are increasingly used to take consequential decisions in problems such as...
Algorithmic fairness research is currently receiving significant attention, aiming to ensure that al...
The data science era is characterized by data-driven automated decision systems (ADS) enabling,...
International audienceFairness of algorithms is the subject of a large body of literature, of guides...
Notwithstanding the widely held view that data generation and data curation processes are prominent ...
Algorithms can now identify patterns and correlations in the (big) datasets, and predict outcomes ba...