mlsanityData to 7 Oct 2026

Model Cards for Model Reporting

arXiv 1810.03993 ↗First posted 5 Oct 2018cs.LG cs.AIcited by 1,068

Not on the map. Why?

Cited by 39 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Trained machine learning models are increasingly used to perform high-impact tasks in areas such as law enforcement, medicine, education, and employment. In order to clarify the intended use cases of machine learning models and minimize their usage in contexts for which they are not well suited, we recommend that released models be accompanied by documentation detailing their performance characteristics. In this paper, we propose a framework that we call model cards, to encourage such transparent model reporting. Model cards are short documents accompanying trained machine learning models that provide benchmarked evaluation in a variety of conditions, such as across different cultural, demographic, or phenotypic groups (e.g., race, geographic location, sex, Fitzpatrick skin type) and intersectional groups (e.g., age and race, or sex and Fitzpatrick skin type) that are relevant to the intended application domains. Model cards also disclose the context in which models are intended to be used, details of the performance evaluation procedures, and other relevant information. While we focus primarily on human-centered machine learning models in the application fields of computer vision and natural language processing, this framework can be used to document any trained machine learning model. To solidify the concept, we provide cards for two supervised models: One trained to detect smiling faces in images, and one trained to detect toxic comments in text. We propose model cards as a step towards the responsible democratization of machine learning and related AI technology, increasing transparency into how well AI technology works. We hope this work encourages those releasing trained machine learning models to accompany model releases with similar detailed evaluation numbers and other relevant documentation.

Key facts. Cited by 1,068 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Oct 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 123; 2022: 169; 2023: 174; 2024: 154; 2025: 178; 2026: 270.

1,068 papers.

Sort:

By area:

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  2. 2023Segment Anythingcited by 8,158
  3. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  4. 2022Flamingo: a Visual Language Model for Few-Shot Learningcited by 4,337
  5. 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,131
  6. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,464
  7. 2023Sigmoid Loss for Language Image Pre-Trainingcited by 3,048
  8. 2022Scaling Instruction-Finetuned Language Modelscited by 2,997
  9. 2022Training Compute-Optimal Large Language Modelscited by 2,927
  10. 2024SAM 2: Segment Anything in Images and Videoscited by 2,784
  11. 2021CLIPScore: A Reference-free Evaluation Metric for Image Captioningcited by 2,388
  12. 2023Code Llama: Open Foundation Models for Codecited by 2,105
38 more
  1. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,968
  2. 2022Large Language Models Encode Clinical Knowledgecited by 1,807
  3. 2022Scaling Autoregressive Models for Content-Rich Text-to-Image Generationcited by 1,287
  4. 2023PaLM 2 Technical Reportcited by 1,154
  5. 2022A Generalist Agentcited by 802
  6. 2023StarCoder: may the source be with you!cited by 790
  7. 2022PaLI: A Jointly-Scaled Multilingual Language-Image Modelcited by 762
  8. 2023The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Onlycited by 755
  9. 2023Scaling Vision Transformers to 22 Billion Parameterscited by 690
  10. 2023A decoder-only foundation model for time-series forecastingcited by 650
  11. 2023Bias and Fairness in Large Language Models: A Surveycited by 626
  12. 2021AI and the Everything in the Whole Wide World Benchmarkcited by 340
  13. 2023Scaling Open-Vocabulary Object Detectioncited by 335
  14. 2023Mathematical Capabilities of ChatGPTcited by 263
  15. 2022Red-Teaming the Stable Diffusion Safety Filtercited by 257
  16. 2021Sustainable AI: Environmental Implications, Challenges and Opportunitiescited by 242
  17. 2021The GEM Benchmark: Natural Language Generation, its Evaluation and Metricscited by 239
  18. 2022Quark: Controllable Text Generation with Reinforced Unlearningcited by 236
  19. 2021Trustworthy AI: From Principles to Practicescited by 206
  20. 2022BlenderBot 3: a deployed conversational agent that continually learns to responsibly engagecited by 197
  21. 2022Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AIcited by 169
  22. 2023Model evaluation for extreme riskscited by 167
  23. 2022Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Textcited by 154
  24. 2023Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Modelscited by 144
  25. 2021MultiBench: Multiscale Benchmarks for Multimodal Representation Learningcited by 143
  26. 2021Can Machines Learn Morality? The Delphi Experimentcited by 140
  27. 2022Natural Language Descriptions of Deep Visual Featurescited by 136
  28. 2022DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Realitycited by 136
  29. 2024The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Modelscited by 136
  30. 2023Auditing large language models: a three-layered approachcited by 135
  31. 2023Harms from Increasingly Agentic Algorithmic Systemscited by 134
  32. 2021Toward a Perspectivist Turn in Ground Truthing for Predictive Computingcited by 129
  33. 2021Human-Centered Explainable AI (XAI): From Algorithms to User Experiencescited by 123
  34. 2024Black-Box Access is Insufficient for Rigorous AI Auditscited by 123
  35. 2021Epistemic Neural Networkscited by 120
  36. 2021What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Researchcited by 119
  37. 2023AI Transparency in the Age of LLMs: A Human-Centered Research Roadmapcited by 119
  38. 2022Vision-Language Pre-training: Basics, Recent Advances, and Future Trendscited by 117

50 most cited of 1,068 All 1,068 (JSON)