Collaborative human-AI risk annotation: co-annotating online incivility with CHAIRA

Introduction. Collaborative human-AI annotation is a promising approach for various tasks with large-scale and complex data. Tools and methods to support effective human-AI collaboration for data annotation are an important direction for research. In this paper, we present CHAIRA: a Collaborative Human-AI Risk Annotation tool that enables human and AI agents to collaboratively annotate online incivility. Method. We leveraged Large Language Models (LLMs) to facilitate the interaction between human and AI annotators and examine four different prompting strategies. The developed CHAIRA system combines multiple prompting approaches with human-AI collaboration for online incivility data annotation. Analysis. We evaluated CHAIRA on 457 user comments with ground truth labels based on the inter-rater agreement between human and AI coders. Results. We found that the most collaborative prompt supported a high level of agreement between a human agent and AI, comparable to that of two human coders. While the AI missed some implicit incivility that human coders easily identified, it also spotted politically nuanced incivility that human coders overlooked. Conclusions. Our study reveals the benefits and challenges of using AI agents for incivility annotation and provides design implications and best practices for human-AI collaboration in subjective data annotation.