A Framework for Automated Measurement of Responsible AI Harms in Generative AI Applications

• URL: http://arxiv.org/abs/2310.17750v1
• Date: Thu, 26 Oct 2023 19:45:06 GMT
• Title: A Framework for Automated Measurement of Responsible AI Harms in Generative AI Applications
• Authors: Ahmed Magooda, Alec Helyar, Kyle Jackson, David Sullivan, Chad Atalla, Emily Sheng, Dan Vann, Richard Edgar, Hamid Palangi, Roman Lutz, Hongliang Kong, Vincent Yun, Eslam Kamal, Federico Zarfati, Hanna Wallach, Sarah Bird, Mei Chen
• Abstract summary: We present a framework for the automated measurement of responsible AI (RAI) metrics for large language models (LLMs) Our framework for automatically measuring harms from LLMs builds on existing technical and sociotechnical expertise. We use this framework to run through several case studies investigating how different LLMs may violate a range of RAI-related principles.
• Score: 15.087045120842207
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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