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

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arxiv 2310.17750 v1 pith:JAVGXFWL submitted 2023-10-26 cs.CL

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

classification cs.CL
keywords frameworkllmsmeasurementresponsibleautomatedexpertiseharmharms
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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