ExploreGen: Large Language Models for Envisioning the Uses and Risks of AI Technologies

TL;DR

ExploreGen leverages large language models to assist AI practitioners in envisioning potential uses and risks of AI technologies, supporting responsible design and compliance through realistic scenario generation and risk classification.

Contribution

This paper introduces ExploreGen, a novel LLM framework that generates diverse AI use cases and assesses their risks, aiding early-stage AI development and compliance efforts.

Findings

ExploreGen generates realistic AI use cases with 94.5% accuracy in risk classification.

Practitioners find ExploreGen helpful for envisioning overlooked uses and assessing risks.

Generated uses are rated as having high adoption potential and transformational impact.

Abstract

Responsible AI design is increasingly seen as an imperative by both AI developers and AI compliance experts. One of the key tasks is envisioning AI technology uses and risks. Recent studies on the model and data cards reveal that AI practitioners struggle with this task due to its inherently challenging nature. Here, we demonstrate that leveraging a Large Language Model (LLM) can support AI practitioners in this task by enabling reflexivity, brainstorming, and deliberation, especially in the early design stages of the AI development process. We developed an LLM framework, ExploreGen, which generates realistic and varied uses of AI technology, including those overlooked by research, and classifies their risk level based on the EU AI Act regulation. We evaluated our framework using the case of Facial Recognition and Analysis technology in nine user studies with 25 AI practitioners. Our findings show that ExploreGen is helpful to both developers and compliance experts. They rated the uses as realistic and their risk classification as accurate (94.5%). Moreover, while unfamiliar with many of the uses, they rated them as having high adoption potential and transformational impact.