Crowd-Assisted Human-AI Teaming with Explanations

This project investigates the problem of information integrity, that is, identifying faulty or ungrounded information online. It focuses on a specific domain, that of information produced during the COVID-19 pandemic, and processes both text and image data. While significant efforts in artificial intelligence (AI) and machine learning (ML) have addressed information integrity in this type of multimodal setting, many solutions cannot be directly applied due to lack of domain specific knowledge and the expertise needed to provide meaningful, convincing explanations. Motivated by such limitations, this project develops a crowd worker-based interactive AI system that explores the collective strengths of the professional knowledge of domain expert crowd workers, the general logical reasoning ability of non-expert crowd workers, and the effective information retrieval capability of AI models. The resulting system will accurately assess information integrity in posts on COVID-19 and explicitly explain the detection results in natural language. This project complements two past research threads: (1) The prevailing AI solutions that primarily focus on extracting specific segments of input posts to serve as explanations, but fail to generate convincing explanations; and (2) Solutions that employ crowd workers, but only recruit non-expert crowd workers and so fail to leverage the domain knowledge of experts. The results of the project will provide unprecedented accuracy by integrating diverse human and machine intelligence to address highly technical, domain-specific problems. While the focus is COVID-19, the framework and models developed in this project will address information integrity with explanations in other domains (such as those in healthcare and public safety). This project will also provide opportunities for students in STEM and underrepresented groups to study human-centered AI techniques.