ML Basis for Intelligence Augmentation:Toward Personalized Modeling, Reasoning under Data-Knowledge Symbiosis, and Interpretable Interaction for AI-assisted Human Decision-making

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

Much of the work people do today—in healthcare, business, scientific enterprises, and military operations—is performed in teams. Collaborative decision-making effort within a team is a complex and challenging process of integrating, understanding, and acting upon different types of information. This project aims to advance the use of artificial intelligence and machine learning as intelligence augmentation (IA) tools for facilitating and improving collaborative decision making in clinical teams, focusing on AI-assisted diagnosis and treatment. The focus of the investigators reflects the practical importance and impact of IA in healthcare, especially in the on-going fight with the pandemic where efficiency, validity, and cost-effectiveness of medical decision-making is critical. However, the proposed methods will apply to other forms and use-cases of IA, such as policy making, public health responses, intelligence and business operations, ultimately advancing national health, prosperity, and welfare. Although modern machine learning research has been widely involved in solving various pattern discovery and recognition tasks based on a wide spectrum of data—either in a fully autonomous fashion or in rudimentary human-AI collaborative settings such as crowdsourcing—effectively augmenting and assisting complex collaborative human decision-making efforts in the space of diagnosis, treatment, planning, logistics remains to be an open challenge. In clinical decision- making, understanding and treating the disease must rely on the vast knowledge and expertise and be based on evidence coming from heterogeneous sources of information, ranging from text (medical history), to imagery (radiograms), to time series data (vitals). Making sense of such multimodal information requires effective communication and collaboration within clinical teams. The investigators propose to study some of the key technical challenges in machine learning for IA: (1) modeling human decision-making processes; (2) incorporating background knowledge into data-driven systems; and (3) building human-AI interface for productive inter- and intra-team collaboration. To that end, the investigators will: (1) develop a machine learning framework based on modeling individual decision-makers that enables accurate detection of errors in medical diagnosis and can be used as a recommendation engine in collaborative decision-making settings; (2) develop principled strategies for integrating objective medical knowledge (e.g., automatically extracted from rapidly growing medical literature) with the clinical experience and expertise of a team of health professionals; (3) design human-interpretable interfaces that enable efficient communication in decision making within and across teams, including new tools for interpreting how the models arrived at each recommended decision and natural language interfaces that can facilitate human-AI collaboration. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.