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Documentation plays a crucial role in both external accountability and internal governance of AI systems.
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FactSheets: Increasing Trust in AI Services through Supplier’s Declarations of Conformity
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Nutritional Labels for Data and Models
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Datasheets for Datasets Help ML Engineers Notice and Understand Ethical Issues in Training Data
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Reusable Templates and Guides For Documenting Datasets and Models for Natural Language Processing and Generation: A Case Study of the HuggingFace and GEM Data and Model Cards. In Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021) . 121–135
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Documenting Computer Vision Datasets: An Invitation to Reflexive Data Practices. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency . ACM, Virtual Event Canada, 161–172
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Where responsible AI meets reality: Practitioner perspectives on enablers for shifting organizational practices
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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
Reward Reports for Reinforcement Learning
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Data Portraits: Recording Foundation Model Training Data
Marc Marone and Benjamin Van Durme. 2023 · 2023
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The Landscape of Data and AI Documentation Approaches in the European Policy Context
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Auditing Large Language Models: A Three-Layered Approach
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Nithya Sambasivan, Shivani Kapania, Hannah Highfill, Diana Akrong, Praveen Paritosh, and Lora M Aroyo. 2021 · 2021
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Jerrold Soh. 2021 · 2021
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Artsheets for Art Datasets. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021)
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Beyond expertise and roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and Their Needs. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems . 1–16
Harini Suresh, Steven R Gomez, Kevin K Nam, and Arvind Satyanarayan. 2021 · 2021
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DAG Card Is the New Model Card
Jacopo Tagliabue, Ville Tuulos, Ciro Greco, and Valay Dave. 2021 · 2021
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Prescriptive and Descriptive Approaches to Machine-Learning Transparency. In CHI Conference on Human Factors in Computing Systems Extended Abstracts . 1–9
David Adkins, Bilal Alsallakh, Adeel Cheema, Narine Kokhlikyan, Emily McReynolds, Pushkar Mishra, Chavez Procope, Jeremy Sawruk, Erin Wang, and Polina Zvyagina. 2022 · 2022
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Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach
Nathalie Baracaldo, Ali Anwar, Mark Purcell, Ambrish Rawat, Mathieu Sinn, Bashar Altakrouri, Dian Balta, et al · 2022
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Understanding Implementation Challenges in Machine Learning Documentation. In Proceedings of the 2nd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization . ACM, Arlington VA USA, 1–8
Jiyoo Chang and Christine Custis. 2022 · 2022
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Jakob Mökander, Jonas Schuett, Hannah Rose Kirk, and Luciano Floridi. 2023 · 2023
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fAIlureNotes: Supporting Designers in Understanding the Limits of AI Models for Computer Vision Tasks
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GPT-4V(Ision) System Card
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Augmented Datasheets for Speech Datasets and Ethical Decision-Making. In 2023 ACM Conference on Fairness, Accountability, and Transparency . ACM, Chicago IL USA, 881–904
Orestis Papakyriakopoulos, Anna Seo Gyeong Choi, William Thong, Dora Zhao, Jerone Andrews, Rebecca Bourke, Alice Xiang, and Allison Koenecke. 2023 · 2023
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Fine-Tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!
Xiangyu Qi, Yi Zeng, Tinghao Xie, Pin-Yu Chen, Ruoxi Jia, Prateek Mittal, and Peter Henderson. 2023 · 2023
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Right the Docs: Characterising Voice Dataset Documentation Practices Used in Machine Learning
Kathy Reid and Elizabeth T. Williams. 2023 · 2023
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Open Datasheets: Machine-Readable Documentation for Open Datasets and Responsible AI Assessments
Anthony Cintron Roman, Jennifer Wortman Vaughan, Valerie See, Steph Ballard, Nicolas Schifano, Jehu Torres, Caleb Robinson, and Juan M. Lavista Ferres. 2023 · 2023
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Evaluating the Social Impact of Generative AI Systems in Systems and Society
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Sociotechnical Safety Evaluation of Generative AI Systems
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