Expertise Trees Resolve Knowledge Limitations in Collective Decision-Making

Research output: Chapter in Book/Report/Conference proceedingConference paper


Experts advising decision-makers are likely to display expertise which varies as a function of the problem instance. In practice, this may lead to sub-optimal or discriminatory decisions against minority cases. In this work, we model such changes in depth and breadth of knowledge as a partitioning of the problem space into regions of differing expertise. We provide here new algorithms that explicitly consider and adapt to the relationship between problem instances and experts’ knowledge. We first propose and highlight the drawbacks of a naive approach based on nearest neighbor queries. To address these drawbacks we then introduce a novel algorithm — expertise trees — that constructs decision trees enabling the learner to select appropriate models. We provide theoretical insights and empirically validate the improved performance of our novel approach on a range of problems for which existing methods proved to be inadequate.
Original languageEnglish
Title of host publicationProceedings of the 40th International Conference on Machine Learning
Number of pages12
ISBN (Print)2640-3498
Publication statusPublished - Jul 2023
EventInternational Conference on Machine Learning - Hawaiʻi Convention Center, Honolulu, United States
Duration: 23 Jul 202329 Jul 2023


ConferenceInternational Conference on Machine Learning
Country/TerritoryUnited States
Internet address


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