Avoiding Decision Fatigue Potential with AI-Assisted Decision-Making

During online browsing, e.g. when looking to select a movie to watch, we are often confronted with multiple rejection-selection steps which can lead to tens or hundreds of decisions made in quick succession. It is unclear if showing the next "best" item, as often employed by standard recommenders, is the most efficient way to help users select an item. In this work, we show that we can reduce the number of decisions to selection with a reinforcement learning-based Decision Minimizer Network (DMN). By implementing a step-aware reward function we can penalize long sequences, leading to fewer decisions having to be made by humans. Using a task to select a movie to watch, we show that we can reduce the number of decisions to selection by 39% compared to heuristic strategies and by 20% compared to standard recommender while increasing user selection satisfaction. Minimizing the number of decision steps can finally help to reduce decision fatigue, which refers to the deteriorating quality of decisions made by an individual after a long session of decision steps, and help to prevent infinite scrolling.