In this position paper, we discuss the potential for leveraging LLMs as interactive research tools to facilitate collaboration between human coders and AI to effectively annotate online risk data at scale. Collaborative human-AI labeling is a promising approach to annotating large-scale and complex data for various tasks. Yet, tools and methods to support effective human-AI collaboration for data annotation are under-studied. This gap is pertinent because co-labeling tasks need to support a two-way interactive discussion that can add nuance and context, particularly in the context of online risk, which is highly subjective and contextualized. Therefore, we provide some of the early benefits and challenges of using LLMs-based tools for risk annotation and suggest future directions for the HCI research community to leverage LLMs as research tools to facilitate human-AI collaboration in contextualized online data annotation. Our research interests align very well with the purposes of the LLMs as Research Tools workshop to identify ongoing applications and challenges of using LLMs to work with data in HCI research. We anticipate learning valuable insights from organizers and participants into how LLMs can help reshape the HCI community's methods for working with data.

翻译：在本立场论文中，我们探讨了利用大型语言模型作为交互式研究工具，促进人类编码者与人工智能之间的协作，以有效开展大规模在线风险数据标注的潜力。人机协作标注是完成各类任务中大规模复杂数据标注的一种有前景的方法。然而，支持高效人机协作数据标注的工具与方法目前尚缺乏研究。这一研究空白具有现实意义，因为协同标注任务需要支持双向交互式讨论以补充细节和语境，尤其是在高度主观且依赖情境的在线风险数据标注场景中。因此，我们总结了基于LLM的工具在风险标注中的初步优势与挑战，并为HCI研究社区提出了未来方向——将LLM作为研究工具，促进在线情境化数据标注中的人机协作。我们的研究兴趣与"LLM作为研究工具"研讨会的主旨高度契合，旨在识别HCI研究中运用LLM处理数据的当前应用与挑战。我们期待从组织者和与会者中获取宝贵见解，了解LLM如何重塑HCI社区的数据处理方法。