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

arXiv: https://arxiv.org/abs/2407.12454 · HTML: https://mlsanity.com/paper/2407.12454/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2024.

## Built on by (most cited first, 12 of 12)

- [Co-designing an AI Impact Assessment Report Template with AI Practitioners and AI Compliance Experts](https://mlsanity.com/paper/2407.17374/) (2024)
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- [AIReg-Bench: Benchmarking Language Models That Assess AI Regulation Compliance](https://mlsanity.com/paper/2510.01474/) (2025)
- [Privy: Envisioning and Mitigating Privacy Risks for Consumer-facing AI Product Concepts](https://mlsanity.com/paper/2509.23525/) (2025)
- [The AI Model Risk Catalog: What Developers and Researchers Miss About Real-World AI Harms](https://mlsanity.com/paper/2508.16672/) (2025)
- [How Well Can LLM Agents Simulate End-User Security and Privacy Attitudes and Behaviors?](https://mlsanity.com/paper/2602.18464/) (2026)
- [Envisioning Stakeholder-Action Pairs to Mitigate Negative Impacts of AI: A Participatory Approach to Inform Policy Making](https://mlsanity.com/paper/2502.14869/) (2025)
- [Scenarios in Computing Research: A Systematic Review of the Use of Scenario Methods for Exploring the Future of Computing Technologies in Society](https://mlsanity.com/paper/2506.05605/) (2025)
- [Agent-Supported Foresight for AI Systemic Risks: AI Agents for Breadth, Experts for Judgment](https://mlsanity.com/paper/2602.08565/) (2026)
- [Towards Leveraging News Media to Support Impact Assessment of AI Technologies](https://mlsanity.com/paper/2411.02536/) (2024)
- [Telling Speculative Stories to Help Humans Imagine the Harms of Healthcare AI](https://mlsanity.com/paper/2510.14718/) (2025)
- [Small-world Networks of Agents Brainstorm AI Risks to Support Ideation](https://mlsanity.com/paper/2609.24859/) (2026)

## 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.