On the Readiness of Scientific Data for a Fair and Transparent Use in
  Machine Learning
Joan Giner-Miguelez Abel Gómez Jordi Cabot
To ensure the fairness and trustworthiness of machine learning (ML) systems,
recent legislative initiatives and relevant research in the ML community have
pointed out the need to document the data used to train ML models. Besides,
data-sharing practices in many scientific domains have evolved in recent years
for reproducibility purposes. In this sense, the adoption of these practices by
academic institutions has encouraged researchers to publish their data and
technical documentation in peer-reviewed publications such as data papers. In
this study, we analyze how this scientific data documentation meets the needs
of the ML community and regulatory bodies for its use in ML technologies. We
examine a sample of 4041 data papers of different domains, assessing their
completeness and coverage of the requested dimensions, and trends in recent
years, putting special emphasis on the most and least documented dimensions. As
a result, we propose a set of recommendation guidelines for data creators and
scientific data publishers to increase their data's preparedness for its
transparent and fairer use in ML technologies.