Evaluating the Capabilities of LLMs for Supporting Anticipatory Impact Assessment
Mowafak Allaham, Nicholas Diakopoulos
TL;DR
This paper investigates whether Large Language Models can support anticipatory governance by generating negative impacts of AI. It compares small, open-source models (GPT-3 and Mistral-7B) fine-tuned on a diverse news-media corpus with instruction-based models (GPT-4 and Mistral-7B-Instruct), evaluating quality, coverage, and biases through a rigorous rubric and a 10-category typology. The results show fine-tuned models achieve comparable quality to GPT-4 and cover a broader range of impact categories, revealing biases in larger models that can overlook certain categories. The work demonstrates a scalable, accessible pathway to assist researchers, journalists, and policymakers in brainstorming AI risks, while highlighting the need to consider data-source biases in anticipatory governance tools.
Abstract
Gaining insight into the potential negative impacts of emerging Artificial Intelligence (AI) technologies in society is a challenge for implementing anticipatory governance approaches. One approach to produce such insight is to use Large Language Models (LLMs) to support and guide experts in the process of ideating and exploring the range of undesirable consequences of emerging technologies. However, performance evaluations of LLMs for such tasks are still needed, including examining the general quality of generated impacts but also the range of types of impacts produced and resulting biases. In this paper, we demonstrate the potential for generating high-quality and diverse impacts of AI in society by fine-tuning completion models (GPT-3 and Mistral-7B) on a diverse sample of articles from news media and comparing those outputs to the impacts generated by instruction-based (GPT-4 and Mistral-7B-Instruct) models. We examine the generated impacts for coherence, structure, relevance, and plausibility and find that the generated impacts using Mistral-7B, a small open-source model fine-tuned on impacts from the news media, tend to be qualitatively on par with impacts generated using a more capable and larger scale model such as GPT-4. Moreover, we find that impacts produced by instruction-based models had gaps in the production of certain categories of impacts in comparison to fine-tuned models. This research highlights a potential bias in the range of impacts generated by state-of-the-art LLMs and the potential of aligning smaller LLMs on news media as a scalable alternative to generate high quality and more diverse impacts in support of anticipatory governance approaches.