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Found 4 papers, 2 papers with code
Using Large Language Models to Enrich the Documentation of Datasets for Machine Learning
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.
Croissant: A Metadata Format for ML-Ready Datasets
Data is a critical resource for Machine Learning (ML), yet working with data remains a key friction point.
On the Readiness of Scientific Data for a Fair and Transparent Use in Machine Learning
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.
A domain-specific language for describing machine learning datasets
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.
