Constructing AI ethics narratives based on real-world data: Human-AI collaboration in data-driven visual storytelling

TL;DR

This paper proposes a data-driven, human-AI collaborative approach to creating authentic AI ethics narratives through visual storytelling, aiming to enhance public understanding and policy development.

Contribution

It introduces a novel framework for human-AI collaboration in visual storytelling about AI ethics, leveraging generative AI to produce genuine narratives based on real-world data.

Findings

Developed a conceptual framework for human-AI collaboration in storytelling

Implemented the framework in a real AI news case

Promoted active public engagement with AI ethics narratives

Abstract

AI ethics narratives have the potential to shape the public accurate understanding of AI technologies and promote communication among different stakeholders. However, AI ethics narratives are largely lacking. Existing limited narratives tend to center on works of science fiction or corporate marketing campaigns of large technology companies. Misuse of "socio-technical imaginary" can blur the line between speculation and reality for the public, undermining the responsibility and regulation of technology development. Therefore, constructing authentic AI ethics narratives is an urgent task. The emergence of generative AI offers new possibilities for building narrative systems. This study is dedicated to data-driven visual storytelling about AI ethics relying on the human-AI collaboration. Based on the five key elements of story models, we proposed a conceptual framework for human-AI collaboration, explored the roles of generative AI and humans in the creation of visual stories. We implemented the conceptual framework in a real AI news case. This research leveraged advanced generative AI technologies to provide a reference for constructing genuine AI ethics narratives. Our goal is to promote active public engagement and discussions through authentic AI ethics narratives, thereby contributing to the development of better AI policies.