Artificial intelligence enables the emergence of novel intelligent decision support systems (IDSSs). Despite the potential for increased efficiency, mixed evidence on user aversion to or appreciation for such intelligent systems prevails, questioning user trust in algorithmic decision support. Recent advances in machine learning facilitate the incorporation of a promising driver of trust into the systems: the systems’ ability to learn. In this study, we conduct an experiment, manipulating the type of decision support (human vs. automated) and their learning ability in the context of a clinical decision support system. Results indicate increased trust in automated decision support with the ability to learn. Our findings contribute to theory and practice, identifying (machine) learning as an antecedent of trust, thereby enhancing our understanding of user perceptions of IDSSs. Furthermore, we add to literature on algorithm aversion by showing that people readily rely on algorithmic support in the context of clinical decision making.
Lohoff, Laura and Rühr, Alexander, "Introducing (Machine) Learning Ability as Antecedent of Trust in Intelligent Systems" (2021). ECIS 2021 Research Papers. 23.
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