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The unstructured nature of data used in foundation model development is a challenge to systematic analyses for making data use and documentation decisions.
Language models are few-shot learners
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Deduplicating training data makes language models better
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Sina Ahmadi and Mariam Masoud. 2020 · 2020
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These Nudes Do Not Exist and I Don’t Know Why This Startup Does Either. In Vice
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Reducing Age Bias in Machine Learning: An Algorithmic Approach
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Realtoxicityprompts: Evaluating neural toxic degeneration in language models
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Towards a critical race methodology in algorithmic fairness. In Proceedings of the 2020 conference on fairness, accountability, and transparency . 501–512
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Social biases in NLP models as barriers for persons with disabilities
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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
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Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 conference on fairness, accountability, and transparency . 33–44
Inioluwa Deborah Raji, Andrew Smart, Rebecca N White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, and Parker Barnes. 2020b · 2020
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What’s in the Box? A Preliminary Analysis of Undesirable Content in the Common Crawl Corpus
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Findings of the WOAH 5 shared task on fine grained hateful memes detection. In Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021) . 201–206
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Who Decides if AI is Fair? The Labels Problem in Algorithmic Auditing
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Understanding the Representation and Representativeness of Age in AI Data Sets. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society . 834–842
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Data and its (dis)contents: A survey of dataset development and use in machine learning research
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Re-imagining algorithmic fairness in india and beyond. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency . 315–328
Nithya Sambasivan, Erin Arnesen, Ben Hutchinson, Tulsee Doshi, and Vinodkumar Prabhakaran. 2021a · 2021
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“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI. In proceedings of the 2021 CHI Conference on Human Factors in Computing Systems . 1–15
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Laion-400m: Open dataset of clip-filtered 400 million image-text pairs
Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, and Aran Komatsuzaki. 2021 · 2021
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Artsheets for Art Datasets
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Ethical and social risks of harm from language models
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Understanding Implementation Challenges in Machine Learning Documentation
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Crowdworksheets: Accounting for individual and collective identities underlying crowdsourced dataset annotation. In 2022 ACM Conference on Fairness, Accountability, and Transparency . 2342–2351
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The Conflict Between Explainable and Accountable Decision-Making Algorithms. In 2022 ACM Conference on Fairness, Accountability, and Transparency . 2103–2113
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