Expertise Trees Resolve Knowledge Limitations in Collective Decision-Making

Axel Abels, Tom Lenaerts, Vito Trianni, Ann Nowe
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:79-90, 2023.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v202-abels23a, title = {Expertise Trees Resolve Knowledge Limitations in Collective Decision-Making}, author = {Abels, Axel and Lenaerts, Tom and Trianni, Vito and Nowe, Ann}, booktitle = {Proceedings of the 40th International Conference on Machine Learning}, pages = {79--90}, year = {2023}, editor = {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan}, volume = {202}, series = {Proceedings of Machine Learning Research}, month = {23--29 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v202/abels23a/abels23a.pdf}, url = {https://proceedings.mlr.press/v202/abels23a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Expertise Trees Resolve Knowledge Limitations in Collective Decision-Making %A Axel Abels %A Tom Lenaerts %A Vito Trianni %A Ann Nowe %B Proceedings of the 40th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Andreas Krause %E Emma Brunskill %E Kyunghyun Cho %E Barbara Engelhardt %E Sivan Sabato %E Jonathan Scarlett %F pmlr-v202-abels23a %I PMLR %P 79--90 %U https://proceedings.mlr.press/v202/abels23a.html %V 202 %X 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.
APA
Abels, A., Lenaerts, T., Trianni, V. & Nowe, A.. (2023). Expertise Trees Resolve Knowledge Limitations in Collective Decision-Making. Proceedings of the 40th International Conference on Machine Learning, in Proceedings of Machine Learning Research 202:79-90 Available from https://proceedings.mlr.press/v202/abels23a.html.

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