Expertise Trees Resolve Knowledge Limitations in Collective Decision-Making

Onderzoeksoutput: Conference 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.
Originele taal-2English
TitelProceedings of the 40th International Conference on Machine Learning
Aantal pagina's12
ISBN van geprinte versie2640-3498
StatusPublished - jul 2023
EvenementInternational Conference on Machine Learning - Hawaiʻi Convention Center, Honolulu, United States
Duur: 23 jul 202329 jul 2023

Publicatie series

NaamProceedings of Machine Learning Research


ConferenceInternational Conference on Machine Learning
Land/RegioUnited States
Internet adres

Bibliografische nota

Publisher Copyright:
© 2023 Proceedings of Machine Learning Research. All rights reserved.


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