Automatic Generation of Model and Data Cards: A Step Towards Responsible AI

Jiarui Liu, Wenkai Li, Zhijing Jin, Mona Diab


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-written 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.
Anthology ID:
2024.naacl-long.110
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1975–1997
Language:
URL:
https://aclanthology.org/2024.naacl-long.110
DOI:
Bibkey:
Cite (ACL):
Jiarui Liu, Wenkai Li, Zhijing Jin, and Mona Diab. 2024. Automatic Generation of Model and Data Cards: A Step Towards Responsible AI. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 1975–1997, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Automatic Generation of Model and Data Cards: A Step Towards Responsible AI (Liu et al., NAACL 2024)
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https://aclanthology.org/2024.naacl-long.110.pdf
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