# Automatic Generation of Model and Data Cards: A Step Towards Responsible AI Source: [Automatic Generation of Model and Data Cards: A Step Towards Responsible AI]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:56:12.281Z Authors: Jiarui Liu, Wenkai Li, Zhijing Jin, Mona Diab Published: 2024-05-10T06:14:07.000Z ## Abstract In an era of model and data proliferation in machine learning/AI especially marked by the rapid advancement of open-sourced technologies, there arises a critical need for standardized consistent documentation. Our work addresses the information incompleteness in current human-generated model and data cards. We propose an automated generation approach using Large Language Models (LLMs). Our key contributions include the establishment of CardBench, a comprehensive dataset aggregated from over 4.8k model cards and 1.4k data cards, coupled with the development of the CardGen pipeline comprising a two-step retrieval process. Our approach exhibits enhanced completeness, objectivity, and faithfulness in generated model and data cards, a significant step in responsible AI documentation practices ensuring better accountability and traceability. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Model Card Generation on MetaGAI-Bench Model Cards](): Model Card Generation; MetaGAI-Bench Model Cards; Faithfulness (G1) Rank: 3. - [Data Card Generation on MetaGAI-Bench Data Cards](): Data Card Generation; MetaGAI-Bench Data Cards; Faithfulness Rank: 4. ## Original sources and implementation [Original paper]() [PDF]()