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

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arxiv 2407.12454 v1 pith:UPJMVSB3 submitted 2024-07-17 cs.HC

classification cs.HC
keywords usesexploregenpractitionerstechnologycompliancedesigndevelopersenvisioning
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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  1. Developing a Risk Identification Framework for Foundation Model Uses

    cs.CR 2025-06 conditional novelty 5.0 of 10

    The paper derives four design requirements for use-based foundation model risk identification and presents an initial questionnaire-based framework demonstrated on a visitor-center chatbot example.

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