Supporting Anticipatory Governance using LLMs: Evaluating and Aligning Large Language Models with the News Media to Anticipate the Negative Impacts of AI

Anticipating the negative impacts of emerging AI technologies is a challenge, especially in the early stages of development. An understudied approach to such anticipation is the use of LLMs to enhance and guide this process. Despite advancements in LLMs and evaluation metrics to account for biases in generated text, it is unclear how well these models perform in anticipatory tasks. Specifically, the use of LLMs to anticipate AI impacts raises questions about the quality and range of categories of negative impacts these models are capable of generating. In this paper we leverage news media, a diverse data source that is rich with normative assessments of emerging technologies, to formulate a taxonomy of impacts to act as a baseline for comparing against. By computationally analyzing thousands of news articles published by hundreds of online news domains around the world, we develop a taxonomy consisting of ten categories of AI impacts. We then evaluate both instruction-based (GPT-4 and Mistral-7B-Instruct) and fine-tuned completion models (Mistral-7B and GPT-3) using a sample from this baseline. We find that the generated impacts using Mistral-7B, fine-tuned on impacts from the news media, tend to be qualitatively on par with impacts generated using a larger scale model such as GPT-4. Moreover, we find that these LLMs generate impacts that largely reflect the taxonomy of negative impacts identified in the news media, however the 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 state-of-the-art LLMs when used for anticipating impacts and demonstrates the advantages of aligning smaller LLMs with a diverse range of impacts, such as those reflected in the news media, to better reflect such impacts during anticipatory exercises.