Mapping AI Ethics Narratives: Evidence from Twitter Discourse Between 2015 and 2022

TL;DR

This paper develops a framework to analyze Twitter discourse on AI ethics, transforming fragmented social media data into coherent narratives to enhance public understanding and policy engagement.

Contribution

It introduces a novel method combining neural networks and large language models to map and visualize AI ethics discussions on Twitter.

Findings

Identified key AI ethics topics from Twitter data.

Created coherent narratives from social media discourse.

Provided insights for policy makers on public concerns.

Abstract

Public participation is indispensable for an insightful understanding of the ethics issues raised by AI technologies. Twitter is selected in this paper to serve as an online public sphere for exploring discourse on AI ethics, facilitating broad and equitable public engagement in the development of AI technology. A research framework is proposed to demonstrate how to transform AI ethics-related discourse on Twitter into coherent and readable narratives. It consists of two parts: 1) combining neural networks with large language models to construct a topic hierarchy that contains popular topics of public concern without ignoring small but important voices, thus allowing a fine-grained exploration of meaningful information. 2) transforming fragmented and difficult-to-understand social media information into coherent and easy-to-read stories through narrative visualization, providing a new perspective for understanding the information in Twitter data. This paper aims to advocate for policy makers to enhance public oversight of AI technologies so as to promote their fair and sustainable development.