A Framework for Automated Measurement of Responsible AI Harms in Generative AI Applications
Year: 2023
Type: preprint
Abstract: We present a framework for the automated measurement of responsible AI (RAI) metrics for large language models (LLMs) and associated products and services. Our framework for automatically measuring harms from LLMs builds on existing technical and sociotechnical expertise and leverages the capabilities of state-of-the-art LLMs, such as GPT-4. We use this framework to run through several case studies investigating how different LLMs may violate a range of RAI-related principles. The framework may be employed alongside domain-specific sociotechnical expertise to create measurements for new harm areas in the future. By implementing this framework, we aim to enable more advanced harm measurement efforts and further the responsible use of LLMs.
Source: arXiv (Cornell University)
Authors: Magooda, Ahmed, Helyar, Alec, Jackson, Kyle, Sullivan, David, Atalla, Chad, Sheng, Emily, Vann, Dan, Edgar, Richard, Palangi, Hamid, Lutz, Roman, Kong, Hongliang, Yun, Vincent, Kamal, Eslam, Zarfati, Federico, Wallach, Hanna, Bird, Sarah, Chen, Mei
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4388068395