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