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Paper Type
ERF
Abstract
Algorithms have long outperformed humans in tasks with objective answers. In medicine, finance, chess, and other objective fields, AI have been shown to consistently outperform human cognition. However, algorithms currently underperform human cognition in creative tasks, such as writing fiction or brainstorming ideas. We propose a study that investigates how humans rely on algorithmic and human recommendations differently in creative tasks, and whether that effect changes based on task difficulty. We synthesize the current theoretical landscape and propose what the likely effects of algorithmic versus human recommendations are on cognitive effort, belief change, and confidence in output.
Recommended Citation
Bogert, Eric; Watson, Rick; and Schecter, Aaron, "Algorithmic Appreciation in Creative Tasks" (2020). AMCIS 2020 Proceedings. 7.
https://aisel.aisnet.org/amcis2020/sig_hci/sig_hci/7
Algorithmic Appreciation in Creative Tasks
Algorithms have long outperformed humans in tasks with objective answers. In medicine, finance, chess, and other objective fields, AI have been shown to consistently outperform human cognition. However, algorithms currently underperform human cognition in creative tasks, such as writing fiction or brainstorming ideas. We propose a study that investigates how humans rely on algorithmic and human recommendations differently in creative tasks, and whether that effect changes based on task difficulty. We synthesize the current theoretical landscape and propose what the likely effects of algorithmic versus human recommendations are on cognitive effort, belief change, and confidence in output.
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