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Paper Type
ERF
Abstract
We propose a two by two experiment that investigates how humans respond to recommendations based on the difficulty of the task and the source of the recommendation. The two types of information sources which will provide recommendations in our experiment are algorithms and human crowds. We contribute to the burgeoning discourse on algorithmic appreciation by focusing on crowd counting, a task in which effort is a strong factor in predicting accuracy.
Recommended Citation
Bogert, Eric; Watson, Rick; and Schecter, Aaron, "Algorithmic Appreciation Across Task Difficulty" (2020). AMCIS 2020 Proceedings. 7.
https://aisel.aisnet.org/amcis2020/data_science_analytics_for_decision_support/data_science_analytics_for_decision_support/7
Algorithmic Appreciation Across Task Difficulty
We propose a two by two experiment that investigates how humans respond to recommendations based on the difficulty of the task and the source of the recommendation. The two types of information sources which will provide recommendations in our experiment are algorithms and human crowds. We contribute to the burgeoning discourse on algorithmic appreciation by focusing on crowd counting, a task in which effort is a strong factor in predicting accuracy.
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