Automatic Generation of Model and Data Cards: A Step Towards Responsible AI
This addresses the need for standardized documentation in AI for better accountability and traceability, though it is incremental as it builds on existing card concepts with automation.
The authors tackled the problem of incomplete documentation in machine learning by developing an automated system using Large Language Models to generate model and data cards, achieving enhanced completeness, objectivity, and faithfulness in the generated cards.
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.