2 code implementations • 4 Apr 2024 • Joan Giner-Miguelez, Abel Gómez, Jordi Cabot
Recent regulatory initiatives like the European AI Act and relevant voices in the Machine Learning (ML) community stress the need to describe datasets along several key dimensions for trustworthy AI, such as the provenance processes and social concerns.
1 code implementation • 28 Mar 2024 • Mubashara Akhtar, Omar Benjelloun, Costanza Conforti, Joan Giner-Miguelez, Nitisha Jain, Michael Kuchnik, Quentin Lhoest, Pierre Marcenac, Manil Maskey, Peter Mattson, Luis Oala, Pierre Ruyssen, Rajat Shinde, Elena Simperl, Goeffry Thomas, Slava Tykhonov, Joaquin Vanschoren, Steffen Vogler, Carole-Jean Wu
Data is a critical resource for Machine Learning (ML), yet working with data remains a key friction point.
no code implementations • 18 Jan 2024 • 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.
1 code implementation • 5 Jul 2022 • Joan Giner-Miguelez, Abel Gómez, Jordi Cabot
So far, these proposals are mostly high-level guidelines described in natural language and, as such, they are difficult to formalize and apply to particular datasets.