CAREER: Redesigning the Human-AI Interaction Paradigm for Improving AI-Assisted Decision Making

Purdue University, West Lafayette IN

Investigators

Abstract

Artificial intelligence (AI)-based decision aids are increasingly used to support people working in domains such as finance, law enforcement, education, and cybersecurity. However, human-AI collaborative decision-making performance is often lower than expected. A lack of sufficient and effective engagement between the person and the AI is a fundamental contributor to these disappointing outcomes. Often, decision makers do not engage with the advice provided by the AI system with their full cognitive capacity or, when they do, they engage with it in ways that lead to inappropriate trust and reliance on the system---both under-reliance and over-reliance are a problem. In current practice, AI-based decision aids do not sufficiently encourage human engagement, nor do they take human engagement behaviors into account. This project aims to create radical new designs for human-AI interaction in which AI is engagement-oriented to improve human-AI collaborations in decision-making. This project develops and evaluates three novel human-AI interaction paradigms: (1) Human-Reflective AI interaction, in which AI is designed to increase the decision makers' engagement level by guiding them to critically reflect on the decision process; (2) Human-Adaptive AI interaction, in which AI assistance will be presented in a personalized manner to nudge decision-makers into appropriately engaging with the AI recommendations; and (3) Human-Behavior-aware AI interaction, in which AI is trained to anticipate decision-makers' engagement behavior and optimize for the human-AI team performance. This project will contribute new scientific knowledge about ways to incorporate cognitive factors of decision-makers, including their capabilities and limitations, into the designs of AI-based decision aids to best support decision-makers and increase AI-human team performance. Comprehensive evaluations on the impacts of new human-AI interaction paradigms on usability, user experience, and the human-AI decision-making performance will reveal the strengths and weaknesses of different approaches and inform the selection among them. 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.