Boosting Consumers: Algorithm-Supported Decision-Making under Uncertainty to (Learn to) Navigate Algorithm-Based Decision Environments

Abstract

AbstractFinding information that is quality assured, objectively required, and subjectively needed is essential for consumers navigating uncertain and complex decision environments (e.g., retail or news platforms) and making informed decisions. This task is particularly challenging when algorithms shape environments and choice sets in the providers’ interest. On the other side, algorithms can support consumers’ decision-making under uncertainty when they are transparent and educate their users (boosting). Exemplary, fast-and-frugal decision trees as interpretable models can provide robust classification performance akin to expert advice and be effective when integrated in consumer decision-making. This study’s author provides an overview of expert-driven decision-tree developments from a consumer research project. The developed tools boost consumers making decisions under uncertainty across different domains. Informed decision making in highly uncertain, non-transparent algorithm-controlled decision environments pose a need for applicable and educative tools, which calls for public engagement in their development within the field of consumer education.