# Boosting human decision-making with AI-generated decision aids

arXiv: https://arxiv.org/abs/2203.02776 · HTML: https://mlsanity.com/paper/2203.02776/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 53 references that are in the map.
- First posted on arXiv 5 Mar 2022.

## Builds on (most cited first, 7 of 7)

- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [Explainable Reinforcement Learning: A Survey](https://mlsanity.com/paper/2005.06247/) (2020)
- [Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey](https://mlsanity.com/paper/2108.09003/) (2021)
- [Learning to select computations](https://mlsanity.com/paper/1711.06892/) (2017)
- [Automatic Discovery of Interpretable Planning Strategies](https://mlsanity.com/paper/2005.11730/) (2020)
- [Improving Human Decision-Making by Discovering Efficient Strategies for Hierarchical Planning](https://mlsanity.com/paper/2102.00521/) (2021)
- [Automatic Discovery and Description of Human Planning Strategies](https://mlsanity.com/paper/2109.14493/) (2021)

## Built on by (most cited first, 3 of 3)

- [Superhuman Artificial Intelligence Can Improve Human Decision Making by Increasing Novelty](https://mlsanity.com/paper/2303.07462/) (2023)
- [An intelligent tutor for planning in large partially observable environments](https://mlsanity.com/paper/2302.02785/) (2023)
- [Discovering the curriculum with AI: A proof-of-concept demonstration with an intelligent tutoring system for teaching project selection](https://mlsanity.com/paper/2406.04082/) (2024)

## Abstract (arXiv)

Human decision-making is plagued by many systematic errors. Many of these errors can be avoided by providing decision aids that guide decision-makers to attend to the important information and integrate it according to a rational decision strategy. Designing such decision aids used to be a tedious manual process. Advances in cognitive science might make it possible to automate this process in the future. We recently introduced machine learning methods for discovering optimal strategies for human decision-making automatically and an automatic method for explaining those strategies to people. Decision aids constructed by this method were able to improve human decision-making. However, following the descriptions generated by this method is very tedious. We hypothesized that this problem can be overcome by conveying the automatically discovered decision strategy as a series of natural language instructions for how to reach a decision. Experiment 1 showed that people do indeed understand such procedural instructions more easily than the decision aids generated by our previous method. Encouraged by this finding, we developed an algorithm for translating the output of our previous method into procedural instructions. We applied the improved method to automatically generate decision aids for a naturalistic planning task (i.e., planning a road trip) and a naturalistic decision task (i.e., choosing a mortgage). Experiment 2 showed that these automatically generated decision-aids significantly improved people's performance in planning a road trip and choosing a mortgage. These findings suggest that AI-powered boosting might have potential for improving human decision-making in the real world.