Dealing with Expert Bias in Collective Decision-Making

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Samenvatting

Quite some real-world problems can be formulated as decision-making problems wherein one must repeatedly make an appropriate choice from a set of alternatives. Multiple expert judgments, whether human or artificial, can help in taking correct decisions, especially when exploration of alternative solutions is costly. As expert opinions might deviate, the problem of finding the right alternative can be approached as a collective decision making problem (CDM) via aggregation of independent judgments. Current state-of-the-art approaches focus on efficiently finding the optimal expert, and thus perform poorly if all experts are not qualified or if they display consistent biases, thereby potentially derailing the decision-making process. In this paper, we propose a new algorithmic approach based on contextual multi-armed bandit problems (CMAB) to identify and counteract such biased expertise. We explore homogeneous, heterogeneous and polarized expert groups and show that this approach is able to effectively exploit the collective expertise, outperforming state-of-the-art methods, especially when the quality of the provided expertise degrades. Our novel CMAB-inspired approach achieves a higher final performance and does so while converging more rapidly than previous adaptive algorithms.
Originele taal-2English
Artikelnummer103921
Aantal pagina's22
TijdschriftArtificial Intelligence
Volume320
DOI's
StatusPublished - jul 2023

Bibliografische nota

Funding Information:
A.A. is supported by a FRIA grant (nr. 5200122F ) by the National Fund for Scientific Research (F.N.R.S.) of Belgium. T.L. is supported by the FNRS project with grant numbers 31257234 and 40007793 , the FWO project with grant nr. G.0391.13N , the Service Public de Wallonie Recherche under grant n° 2010235–ARIAC by DigitalWallonia4.ai. T.L and A.N. benefit from the support of the Flemish Government through the AI Research Program. T.L., V.T and A.N. acknowledge the support by TAILOR, a project funded by EU Horizon 2020 research and innovation program under GA No 952215 . The resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation - Flanders ( FWO ) and the Flemish Government .

Publisher Copyright:
© 2023 Elsevier B.V.

Copyright:
Copyright 2023 Elsevier B.V., All rights reserved.

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