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

2025-02-02

AI Summary

Current AI ethics narratives predominantly stem from science fiction or corporate marketing, leading to public misconceptions, ambiguous accountability, and regulatory gaps. To address this, we propose the first data-driven visual narrative system grounded in real-world AI news events. Our method introduces a human-AI collaborative storytelling framework integrating the “five narrative elements” (character, setting, plot, conflict, resolution), explicitly defining complementary roles: human editors provide ethical oversight and structural guidance, while generative AI—supporting multimodal text-to-image synthesis—handles content instantiation and visualization. Leveraging narrative modeling, data-driven visualization, and a structured workflow, the system generates reproducible, high-fidelity ethical narratives across multiple verified cases. Results demonstrate significant improvements in narrative authenticity and public engagement. This work establishes a novel, empirically grounded policy communication paradigm for AI governance—one that bridges rigorous evidence with effective public discourse.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Humans and AI: Game Design — Procedural Content Generation & Storytelling

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

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.

Problem

• AI ethics
• public understanding
• stakeholder communication

Innovation

• Data-Driven Visual Storytelling
• Human-AI Collaboration
• AI Ethics Narratives