Boosting human decision-making with AI-generated decision aids

2022

Article

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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 is 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 has potential for improving human decision-making in the real world.

@article{Becker2022ARX-boosting,
  title = {Boosting human decision-making with AI-generated decision aids},
  author = {Becker, Frederic and Skirzyński, Julian and van Opheusden, Bas and Lieder, Falk},
  journal = {Computational Brain & Behavior},
  volume = {5},
  number = {4},
  pages = {467--490},
  month = jul,
  year = {2022},
  doi = {10.48550/arXiv.2203.02776},
  month_numeric = {7}
}