Generative AI and accuracy in the history of mathematics

ROWLETT, Peter (2024). Generative AI and accuracy in the history of mathematics. British Journal for the History of Mathematics.

Preview	PDF generative-ai.pdf - Accepted Version Creative Commons Attribution. Download (180kB) | Preview
Preview	PDF Rowlett-GenerativeAIAccuracy(VoR).pdf - Published Version Creative Commons Attribution Non-commercial No Derivatives. Download (590kB) | Preview

Abstract

Generative AI systems designed to produce text do so by drawing on inferences made from training data, which may mean they reproduce factual errors or biases contained in that data. This process is illustrated by querying ChatGPT with questions from a history of mathematics quiz designed to highlight the common occurrence of mathematical results being misattributed. ChatGPT's performance on a set of decades-old common misconceptions is mixed, illustrating the potential for these systems to reproduce and reinforce historical inaccuracies and misconceptions.

Item Type:	Article
Identification Number:	https://doi.org/10.1080/26375451.2024.2312789
SWORD Depositor:	Symplectic Elements
Depositing User:	Symplectic Elements
Date Deposited:	29 Jan 2024 16:48
Last Modified:	27 Mar 2024 15:30
URI:	https://shura.shu.ac.uk/id/eprint/33102

Actions (login required)

View Item

Downloads

Publication Metrics