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

Anonymous

TL;DR: We propose a novel task, Model Card Generation, build a knowledge retrieval-based LLM pipeline, and conduct comprehensive evaluations.

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.

Paper Type: long

Research Area: Ethics, Bias, and Fairness

Contribution Types: NLP engineering experiment, Data resources

Languages Studied: English