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On the Readiness of Scientific Data Papers for a Fair and Transparent Use in Machine Learning

Computer Science

On the Readiness of Scientific Data Papers for a Fair and Transparent Use in Machine Learning

J. Giner-miguelez, A. Gómez, et al.

This study analyzes how scientific data documentation aligns with machine learning and regulatory needs for fairness and trustworthiness. By examining 4,041 data papers across domains and comparing them with NeurIPS D&B dataset descriptions, the authors identify coverage gaps and trends and propose practical recommendations to make datasets more transparent and ML-ready. Research conducted by Joan Giner-Miguelez, Abel Gómez, and Jordi Cabot.

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~3 min • Beginner • English
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