"decisionTime"
A Configurable Framework for Reproducible Human-AI Decision-Making Studies
Sara Salimzadeh (TU Delft - Web Information Systems)
U.K. Gadiraju (TU Delft - Web Information Systems)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Empirical studies have extensively investigated human decision-making processes in various domains where AI systems are incorporated. However, comparing and replicating these studies can be challenging due to different experimental configurations. Moreover, the existing contexts often have limited scope and may not fully capture the complexity of real-world decision-making scenarios that are riddled with varying levels of uncertainty. Our framework addresses these practical gaps by providing a configurable and reproducible environment for conducting human-AI decision-making studies in the route planning domain that captures many complexities of real-world scenarios. Researchers can customize parameters, conditions, and factors involved in decision-making tasks to help address research and empirical gaps through rigorous experiments. With various modules such as map generation, chat components, and different AI systems available within the "DecisionTime"framework, researchers can effortlessly design experiments exploring multiple aspects of human-AI interaction and decision-making.