In this section
@inproceedings{Jiang:2024:10.5555/3635637.3662950,
author = {Jiang, J and Rago, A and Leofante, F and Toni, F},
doi = {10.5555/3635637.3662950},
pages = {954--963},
publisher = {ACM},
title = {Recourse under model multiplicity via argumentative ensembling},
url = {http://dx.doi.org/10.5555/3635637.3662950},
year = {2024}
}
TY - CPAPER
AB - Model Multiplicity (MM) arises when multiple, equally performing machine learning models can be trained to solve the same prediction task. Recent studies show that models obtained under MM may produce inconsistent predictions for the same input. When this occurs, it becomes challenging to provide counterfactual explanations(CEs), a common means for offering recourse recommendations to individuals negatively affected by models’ predictions. In this paper, we formalise this problem, which we name recourse-aware ensembling, and identify several desirable properties which methods for solving it should satisfy. We demonstrate that existing ensemblingmethods, naturally extended in different ways to provide CEs, fail to satisfy these properties. We then introduce argumentative ensembling, deploying computational argumentation as a means to guarantee robustness of CEs to MM, while also accommodating customisable user preferences. We show theoretically and experimentally that argumentative ensembling is able to satisfy propertieswhich the existing methods lack, and that the trade-offs are minimal wrt the ensemble’s accuracy.
AU - Jiang,J
AU - Rago,A
AU - Leofante,F
AU - Toni,F
DO - 10.5555/3635637.3662950
EP - 963
PB - ACM
PY - 2024///
SP - 954
TI - Recourse under model multiplicity via argumentative ensembling
UR - http://dx.doi.org/10.5555/3635637.3662950
UR - https://dl.acm.org/doi/10.5555/3635637.3662950
UR - http://hdl.handle.net/10044/1/108973
ER -
Artificial Intelligence Network
South Kensington Campus
Imperial College London
SW7 2AZ
To reach the elected speaker of the network, Dr Rossella Arcucci, please contact:
To reach the network manager, Diana O'Malley - including to join the network - please contact: