ExploreGen: Large Language Models for Envisioning the Uses and Risks of AI Technologies
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Abstract (arXiv)
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.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Jul 2024.
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Citing papers per year: 2024: 2; 2025: 7; 2026: 3.
12 papers.
- 2024Co-designing an AI Impact Assessment Report Template with AI Practitioners and AI Compliance Expertscited by 10
- 2025Should LLMs be WEIRD? Exploring WEIRDness and Human Rights in Large Language Modelscited by 7
- 2025AIReg-Bench: Benchmarking Language Models That Assess AI Regulation Compliancecited by 6
- 2025Privy: Envisioning and Mitigating Privacy Risks for Consumer-facing AI Product Conceptscited by 4
- 2025The AI Model Risk Catalog: What Developers and Researchers Miss About Real-World AI Harmscited by 3
- 2026How Well Can LLM Agents Simulate End-User Security and Privacy Attitudes and Behaviors?cited by 3
- 2025Envisioning Stakeholder-Action Pairs to Mitigate Negative Impacts of AI: A Participatory Approach to Inform Policy Makingcited by 2
- 2025Scenarios in Computing Research: A Systematic Review of the Use of Scenario Methods for Exploring the Future of Computing Technologies in Societycited by 2
- 2026Agent-Supported Foresight for AI Systemic Risks: AI Agents for Breadth, Experts for Judgmentcited by 2
- 2024Towards Leveraging News Media to Support Impact Assessment of AI Technologiescited by 0
- 2025Telling Speculative Stories to Help Humans Imagine the Harms of Healthcare AIcited by 0
- 2026Small-world Networks of Agents Brainstorm AI Risks to Support Ideationcited by 0
- 2026Small-world Networks of Agents Brainstorm AI Risks to Support Ideation21 Sep 2026
- 2026Agent-Supported Foresight for AI Systemic Risks: AI Agents for Breadth, Experts for Judgment9 Feb 2026
- 2026How Well Can LLM Agents Simulate End-User Security and Privacy Attitudes and Behaviors?6 Feb 2026
- 2025Telling Speculative Stories to Help Humans Imagine the Harms of Healthcare AI16 Oct 2025
- 2025AIReg-Bench: Benchmarking Language Models That Assess AI Regulation Compliance1 Oct 2025
- 2025Privy: Envisioning and Mitigating Privacy Risks for Consumer-facing AI Product Concepts27 Sep 2025
- 2025Should LLMs be WEIRD? Exploring WEIRDness and Human Rights in Large Language Models22 Aug 2025
- 2025The AI Model Risk Catalog: What Developers and Researchers Miss About Real-World AI Harms21 Aug 2025
- 2025Scenarios in Computing Research: A Systematic Review of the Use of Scenario Methods for Exploring the Future of Computing Technologies in Society5 Jun 2025
- 2025Envisioning Stakeholder-Action Pairs to Mitigate Negative Impacts of AI: A Participatory Approach to Inform Policy Making24 Jan 2025
- 2024Towards Leveraging News Media to Support Impact Assessment of AI Technologies4 Nov 2024
- 2024Co-designing an AI Impact Assessment Report Template with AI Practitioners and AI Compliance Experts24 Jul 2024