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Human Response to Decision Support in Face Matching: The Influence of Task Difficulty and Machine Accuracy
Marina Estévez-Almenzar, Ricardo Baeza-Yates, Carlos Castillo
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Decision support systems enhanced by Artificial Intelligence (AI) are increasingly being used in high-stakes scenarios where errors or biased outcomes can have significant consequences. In this work, we explore the conditions under which AI-based decision support systems affect the decision accuracy of humans involved in face matching tasks. Previous work suggests that this largely depends on various factors, such as the specific nature of the task and how users perceive the quality of the decision support, among others. Hence, we conduct extensive experiments to examine how both task difficulty and the precision of the system influence human outcomes. Our results show a strong influence of task difficulty, which not only makes humans less precise but also less capable of determining whether the decision support system is yielding accurate suggestions or not. This has implications for the design of decision support systems, and calls for a careful examination of the context in which they are deployed and on how they are perceived by users.
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