Automatic Generation of Model and Data Cards: A Step Towards Responsible AI
<|reference_start|>Automatic Generation of Model and Data Cards: A Step Towards Responsible AI: 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.<|reference_end|>
@article{liu2024automatic,
    title={Automatic Generation of Model and Data Cards: A Step Towards Responsible
  AI},
    author={Jiarui Liu, Wenkai Li, Zhijing Jin, Mona Diab},
    journal={arXiv preprint arXiv:2405.06258},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.06258},
    primaryClass={cs.CL}
}