EAGER: AI-DCL: Hybrid human algorithm predictions: balancing effort, accuracy, and perceived autonomy

点击翻译按钮获取中文摘要

Artificial intelligence algorithms are becoming an increasingly common component in everyday decision-making scenarios. Such algorithms use medical data to assist doctors in making diagnostic and treatment decisions, use sensor data to assist drivers in operating vehicles, and use review data to make recommendations for products to purchase and restaurants to visit. These systems are often hybrid, with decisions influenced by a combination of an artificial intelligence algorithm and a human decision-maker. For example, in one mode of operation, a human makes each decision after taking into account input from the algorithm. In another mode of operation, when human resources are limited (as in medical diagnosis), a hybrid system can use the algorithm to make the majority of routine decisions and allocate to a human decision-maker only those problems that are most challenging to the algorithm. As these hybrid systems are being increasingly used in critical decision-making tasks it is important to address questions about the understanding and design of such systems. This project will address a series of fundamental questions about hybrid decision-making systems from the dual perspectives of algorithm development and cognitive models of human reasoning and decision-making. For example, how can the overall performance of a hybrid human-algorithm system be optimized when taking into account limited human resources and potential trade-offs between human and algorithmic skills? From a psychological perspective, does a perceived lack of autonomy negatively affect human engagement and can systems be designed to mitigate this? The project will develop new theories and methods for how humans and algorithms can work together and the results will help produce more accurate and more robust decision-making systems across a variety of areas such as medicine, transportation, business, and consumer applications. To achieve its goals the research project will bring together prior threads of work from psychology, machine learning, and Bayesian estimation. The project will consist of two closely-coupled components with a common focus on modeling and understanding of prediction problems that are handled by a combination of human and algorithmic expertise. The first component will develop and evaluate different computational and statistical frameworks for an algorithmic arbitrator that balances black-box predictions and human expertise in large-scale classification tasks. The second component will build on theories from human cognition and psychology to analyze joint algorithm-human prediction performance, with explicit consideration of the effect of a human dropping out and not continuing to work with the algorithm (e.g., due to a perceived lack of autonomy). An extensive series of user studies, under a variety of hybrid prediction scenarios and different decision allocation methods, will be conducted during the project to support the development of new cognitive insights and computational approaches for hybrid algorithm-human prediction systems.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.