Human-AI Teaming for Big Data Analytics to Enhance Response to the COVID-19 Pandemic: Online Interviewing Protocol

This research focuses on capturing the ephemeral data from a variety of social media sources and our two research thrusts include: 1) online observations of Community Emergency Response Team (CERT) volunteers and a manager (a collaborator on this project) using think-aloud and cognitive interviewing strategies to reveal the real-time mental models used to make coding decisions for annotation tasks; and 2) an empirical analysis of different sampling algorithms for active (machine) learning paradigms to develop a typology of machine errors under diverse contexts that affect the quality of human decision making for annotation. This research will generate design guidelines that bridge the gap between the mechanisms used for real-time data processing with AI models and the understanding of context contributed by a human user teaming with the AI models. Using theories of human decision-making combined with knowledge of how AI functions, this project provides a real-time, mid-disaster examination of 1) how humans understand, process, and interpret social media messages, and 2) how to refine AI algorithms to optimize active learning paradigm. This understanding will provide a theoretical framework enabling future research to develop protocols to optimize human-AI teaming by using concepts such as motivation and information theory. This work can help emergency managers conduct better training of their CERT volunteers and other annotators and provide clearer guidelines for how to communicate the unique value that humans bring to the annotation process for AI systems. Both our protocols and developed understanding of how humans interact with AI systems will be helpful for global health organizations, local and state-level disaster decision-makers, as well as provide direction for the vast CERT network in the United States.