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DMLR: Data-centric Machine Learning Research -- Past, Present and Future

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arxiv 2311.13028 v2 pith:KZ2TQHOQ submitted 2023-11-21 cs.LG cs.AIcs.DCeess.SP

Luis Oala , Manil Maskey , Lilith Bat-Leah , Alicia Parrish , Nezihe Merve Gürel , Tzu-Sheng Kuo , Yang Liu , Rotem Dror
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Danilo Brajovic Xiaozhe Yao Max Bartolo William A Gaviria Rojas Ryan Hileman Rainier Aliment Michael W. Mahoney Meg Risdal Matthew Lease Wojciech Samek Debojyoti Dutta Curtis G Northcutt Cody Coleman Braden Hancock Bernard Koch Girmaw Abebe Tadesse Bojan Karlaš Ahmed Alaa Adji Bousso Dieng Natasha Noy Vijay Janapa Reddi James Zou Praveen Paritosh Mihaela van der Schaar Kurt Bollacker Lora Aroyo Ce Zhang Joaquin Vanschoren Isabelle Guyon Peter Mattson
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classification cs.LGcs.AIcs.DCeess.SP
keywords creationdatasetsdmlrlearningmachineadvancebusinesschart
verification ladder T0 review T1 audit T2 compute T3 formal
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Drawing from discussions at the inaugural DMLR workshop at ICML 2023 and meetings prior, in this report we outline the relevance of community engagement and infrastructure development for the creation of next-generation public datasets that will advance machine learning science. We chart a path forward as a collective effort to sustain the creation and maintenance of these datasets and methods towards positive scientific, societal and business impact.

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  1. TEDI: Trustworthy and Ethical Dataset Indicators to Analyze and Compare Dataset Documentation

    cs.CY 2025-05 conditional novelty 6.0 of 10

    A new 143-indicator rubric applied to 114 human-voice datasets shows that documentation of consent, privacy, and harmful content is rare, and that scraping yields scale at the cost of documented ethical practices.

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