A new perspective paper argues for "measuring data" as a critical task to advance responsible AI development. Just as physical objects can be measured, data used to train AI systems should also be quantitatively analyzed to understand its composition.

A new study provides insights into the complex processes and people behind ML data work.

In a recent paper, researchers Mehtab Khan and Alex Hanna highlight the need for greater scrutiny, transparency, and accountability in how massive datasets for machine learning models are created.

Machine learning models rely heavily on their training datasets, inheriting inherent biases and limitations. This research proposes "datasheets for datasets" increasing transparency and mitigating risks.