Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies

TL;DR

This survey reviews over 100 empirical studies on human-AI decision making, highlighting current practices, gaps, and future research directions to develop a scientific understanding of human-AI collaboration in decision processes.

Contribution

It provides a comprehensive summary of study designs in human-AI decision making research and offers recommendations for standardizing methodologies and advancing the field.

Findings

Identifies common decision tasks and AI assistance elements used in studies.

Highlights gaps in current research practices and the need for standardized frameworks.

Suggests future directions for empirical research to improve human-AI decision collaboration.

Abstract

As AI systems demonstrate increasingly strong predictive performance, their adoption has grown in numerous domains. However, in high-stakes domains such as criminal justice and healthcare, full automation is often not desirable due to safety, ethical, and legal concerns, yet fully manual approaches can be inaccurate and time consuming. As a result, there is growing interest in the research community to augment human decision making with AI assistance. Besides developing AI technologies for this purpose, the emerging field of human-AI decision making must embrace empirical approaches to form a foundational understanding of how humans interact and work with AI to make decisions. To invite and help structure research efforts towards a science of understanding and improving human-AI decision making, we survey recent literature of empirical human-subject studies on this topic. We summarize the study design choices made in over 100 papers in three important aspects: (1) decision tasks, (2) AI models and AI assistance elements, and (3) evaluation metrics. For each aspect, we summarize current trends, discuss gaps in current practices of the field, and make a list of recommendations for future research. Our survey highlights the need to develop common frameworks to account for the design and research spaces of human-AI decision making, so that researchers can make rigorous choices in study design, and the research community can build on each other's work and produce generalizable scientific knowledge. We also hope this survey will serve as a bridge for HCI and AI communities to work together to mutually shape the empirical science and computational technologies for human-AI decision making.