Accuracy of training data and model outputs in Generative AI: CREATe Response to the Information Commissioner Office Consultation

TL;DR

This paper discusses the importance of accuracy in Generative AI, highlighting issues with training data and hallucinations, and emphasizes regulatory and legal considerations for responsible AI development.

Contribution

It provides an analysis of data accuracy challenges in Generative AI and offers legal and regulatory insights from CREATe's research to inform policy discussions.

Findings

Training data flaws can lead to inaccurate outputs

Hallucinations in AI outputs pose risks to users

Legal frameworks are crucial for responsible AI use

Abstract

The accuracy of Generative AI is increasingly critical as Large Language Models become more widely adopted. Due to potential flaws in training data and hallucination in outputs, inaccuracy can significantly impact individuals interests by distorting perceptions and leading to decisions based on flawed information. Therefore, ensuring these models accuracy is not only a technical necessity but also a regulatory imperative. ICO call for evidence on the accuracy of Generative AI marks a timely effort in ensuring responsible Generative AI development and use. CREATe, as the Centre for Regulation of the Creative Economy based at the University of Glasgow, has conducted relevant research involving intellectual property, competition, information and technology law. We welcome the ICO call for evidence on the accuracy of Generative AI, and we are happy to highlight aspects of data protection law and AI regulation that we believe should receive attention.