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R. Bommasani and C. Cardie · 2020
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Intrinsic evaluation of summarization datasets
R. Bommasani and C. Cardie · 2020
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Lessons from archives: Strategies for collecting sociocultural data in machine learning
E. S. Jo and T. Gebru · 2020
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Diversity, density, and homogeneity: Quantitative characteristic metrics for text collections
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Y.-A. Lai, X. Zhu, Y. Zhang, and M. Diab · 2020
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Deep reinforcement learning at the edge of the statistical precipice
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Measuring model biases in the absence of ground truth
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On the dangers of stochastic parrots: Can language models be too big?
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Multimodal datasets: misogyny, pornography, and malignant stereotypes
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A. Birhane, V. U. Prabhu, and E. Kahembwe · 2021
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Excavating ai: The politics of images in machine learning training sets
K. Crawford and T. Paglen · 2021
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Data-centric ai resource hub, 2021
Data-centric AI · 2021
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T. Gebru, J. Morgenstern, B. Vecchione, J. W. Vaughan, H. Wallach, H. D. Iii, and K. Crawford · 2021
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Know your data, 2021
Google Research · 2021
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A. Z. Jacobs and H. Wallach · 2021
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Deduplicating training data makes language models better
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K. Lee, D. Ippolito, A. Nystrom, C. Zhang, D. Eck, C. Callison-Burch, and N. Carlini · 2021
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A data-centric approach for training deep neural networks with less data
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M. Motamedi, N. Sakharnykh, and T. Kaldewey · 2021
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Data and its (dis) contents: A survey of dataset development and use in machine learning research
A. Paullada, I. D. Raji, E. M. Bender, E. Denton, and A. Hanna · 2021
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“everyone wants to do the model work, not the data work”: Data cascades in high-stakes ai
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Do datasets have politics? disciplinary values in computer vision dataset development
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Laion-400m: Open dataset of clip-filtered 400 million image-text pairs
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C. Schuhmann, R. Vencu, R. Beaumont, R. Kaczmarczyk, C. Mullis, A. Katta, T. Coombes, J. Jitsev, and A. Komatsuzaki · 2021
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Announcing the neurips 2021 datasets and benchmarks track, 2021
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Summvis: Interactive visual analysis of models, data, and evaluation for text summarization
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J. Vig, W. Kryściński, K. Goel, and N. F. Rajani · 2021
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Data collection and quality challenges in deep learning: A data-centric ai perspective
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Tracing knowledge in language models back to the training data
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Clean lab, 2022
Clean Lab · 2022
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Bertin: Efficient pre-training of a spanish language model using perplexity sampling
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J. De la Rosa, E. G. Ponferrada, P. Villegas, P. G. d. P. Salas, M. Romero, and M. Grandury · 2022
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dcbench: a benchmark for data-centric ai systems
S. Eyuboglu, B. Karlaš, C. Ré, C. Zhang, and J. Zou · 2022
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The vendi score: A diversity evaluation metric for machine learning
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D. Friedman and A. B. Dieng · 2022
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Whose language counts as high quality? measuring language ideologies in text data selection
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Data quality for AI tool: Exploratory data analysis on IBM API
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Advances, challenges and opportunities in creating data for trustworthy ai
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Bugs in the data: How imagenet misrepresents biodiversity
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A. S. Luccioni and D. Rolnick · 2022
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Introducing the data measurements tool: an interactive tool for looking at datasets, 2022
S. Luccioni, Y. Jernite, and M. Mitchell · 2022
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Seal : Interactive tool for systematic error analysis and labeling
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N. Rajani, W. Liang, L. Chen, M. Mitchell, and J. Zou · 2022
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Beyond Measure: The Hidden History of Measurement
J. Vincent · 2022
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Datalab: A platform for data analysis and intervention
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