Experimental Investigation of Algorithm Delegation for Choice Tasks
Abstract
Are people willing to delegate their decisions to algorithms? This question is crucial for understanding the economic implications of Al. In this paper, we contribute to answering it by experimentally examining attitudes toward algorithmic delegation, i.e., the willingness to delegate choices to algorithms. Unlike prior research focused on forecasting or judgmental tasks, our study centers on choice tasks, where individuals make decisions based on personal preferences over lotteries. Two opposing forces may drive delegation: the desire for autonomy versus the burden of choice overload. To isolate intrinsic preferences, we equalize error rates across all treatments. Results from a preregistered study show no significant difference in willingness to delegate between human and algorithmic decision-makers, suggesting that people are not generally averse to algorithmic decision-making.
About this workshop
The aim of this workshop is to promote technical and practical exchanges between researchers who use NLP methods. There is no hesitation in detailing the code (r/python), sharing tips, and discovering new methods and models.
Periodicity: Thursdays from 12h15 to 13h30, by videoconference.