mlsanityData to 14 Oct 2026

Data Authenticity, Consent, & Provenance for AI are all broken: what will it take to fix them?

arXiv 2404.12691 ↗First posted 19 Apr 2024cs.AI cs.CYcited by 15

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Abstract (arXiv)

New capabilities in foundation models are owed in large part to massive, widely-sourced, and under-documented training data collections. Existing practices in data collection have led to challenges in tracing authenticity, verifying consent, preserving privacy, addressing representation and bias, respecting copyright, and overall developing ethical and trustworthy foundation models. In response, regulation is emphasizing the need for training data transparency to understand foundation models' limitations. Based on a large-scale analysis of the foundation model training data landscape and existing solutions, we identify the missing infrastructure to facilitate responsible foundation model development practices. We examine the current shortcomings of common tools for tracing data authenticity, consent, and documentation, and outline how policymakers, developers, and data creators can facilitate responsible foundation model development by adopting universal data provenance standards.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 47 of its 104 references that are in the map. First posted on arXiv 19 Apr 2024.

Builds on

47 of 104 references.

References · 47

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2020Scaling Laws for Neural Language Modelscited by 6,586
  3. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  4. 2022Scaling Instruction-Finetuned Language Modelscited by 3,024
  5. 2022Training Compute-Optimal Large Language Modelscited by 3,019
  6. 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,411
  7. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,218
  8. 2020Extracting Training Data from Large Language Modelscited by 2,189
  9. 2022Competition-Level Code Generation with AlphaCodecited by 1,462
  10. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,439
  11. 2022Solving Quantitative Reasoning Problems with Language Modelscited by 1,392
  12. 2018Datasheets for Datasetscited by 1,386
35 more
  1. 2018Model Cards for Model Reportingcited by 1,080
  2. 2023ChatGPT Outperforms Crowd-Workers for Text-Annotation Taskscited by 870
  3. 2023A Watermark for Large Language Modelscited by 725
  4. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  5. 2021Datasets: A Community Library for Natural Language Processingcited by 530
  6. 2021Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpuscited by 492
  7. 2023Scalable Extraction of Training Data from (Production) Language Modelscited by 434
  8. 2023Can AI-Generated Text be Reliably Detected?cited by 386
  9. 2023Towards Expert-Level Medical Question Answering with Large Language Modelscited by 384
  10. 2023A Survey of Hallucination in Large Foundation Modelscited by 368
  11. 2023The Curse of Recursion: Training on Generated Data Makes Models Forgetcited by 356
  12. 2019Measuring Bias in Contextualized Word Representationscited by 276
  13. 2023The Impact of AI on Developer Productivity: Evidence from GitHub Copilotcited by 274
  14. 2023Can Large Language Models Transform Computational Social Science?cited by 250
  15. 2022Is GPT-3 a Good Data Annotator?cited by 208
  16. 2022Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AIcited by 171
  17. 2022Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Datasetcited by 113
  18. 2020Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biasescited by 107
  19. 2023Artificial Artificial Artificial Intelligence: Crowd Workers Widely Use Large Language Models for Text Production Taskscited by 103
  20. 2023Art and the science of generative AI: A deeper divecited by 95
  21. 2021Time Waits for No One! Analysis and Challenges of Temporal Misalignmentcited by 91
  22. 2020Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditingcited by 89
  23. 2023Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Modelscited by 87
  24. 2023The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AIcited by 73
  25. 2023SILO Language Models: Isolating Legal Risk In a Nonparametric Datastorecited by 71
  26. 2022Leakage and the Reproducibility Crisis in ML-based Sciencecited by 65
  27. 2021Changing the World by Changing the Datacited by 51
  28. 2021Addressing "Documentation Debt" in Machine Learning Research: A Retrospective Datasheet for BookCorpuscited by 47
  29. 2021Just What do You Think You're Doing, Dave?' A Checklist for Responsible Data Use in NLPcited by 47
  30. 2023Talkin' 'Bout AI Generation: Copyright and the Generative-AI Supply Chaincited by 46
  31. 2022Data Governance in the Age of Large-Scale Data-Driven Language Technologycited by 33
  32. 2023Ecosystem Graphs: The Social Footprint of Foundation Modelscited by 32
  33. 2023Market Concentration Implications of Foundation Modelscited by 15
  34. 2024Foundation Model Transparency Reportscited by 14
  35. 2023Stronger Together: on the Articulation of Ethical Charters, Legal Tools, and Technical Documentation in MLcited by 9

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 6; 2026: 5.

15 papers.

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  1. 2024Consent in Crisis: The Rapid Decline of the AI Data Commonscited by 46
  2. 2024The 2024 Foundation Model Transparency Indexcited by 19
  3. 2024The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resourcescited by 11
  4. 2025Large Language Models as Psychological Simulators: A Methodological Guidecited by 6
  5. 2026AIBoMGen: Generating an AI Bill of Materials for Secure, Transparent, and Compliant Model Trainingcited by 6
  6. 2025Machine Learning Models Have a Supply Chain Problemcited by 4
  7. 2025On the Coexistence and Ensembling of Watermarkscited by 3
  8. 2025ZKPROV: A Zero-Knowledge Approach to Dataset Provenance for Large Language Modelscited by 2
  9. 2025PETLP: A Privacy-by-Design Pipeline for Social Media Data in AI Researchcited by 1
  10. 2024Building an Ethical and Trustworthy Biomedical AI Ecosystem for the Translational and Clinical Integration of Foundational Modelscited by 0
  11. 2025TEDI: Trustworthy and Ethical Dataset Indicators to Analyze and Compare Dataset Documentationcited by 0
  12. 2026Sovereign Context Protocol: An Open Attribution Layer for Human-Generated Content in the Age of Large Language Modelscited by 0
3 more
  1. 2026AdaQE-CG: Adaptive Query Expansion for Web-Scale Generative AI Model and Data Card Generationcited by 0
  2. 2026A governance horizon for ethical-use constraints in open-weight AI modelscited by 0
  3. 2026AtlasNLP: A Country-Aware Atlas of Dataset Representation in NLPcited by 0