Explaining Preferences with Shapley ValuesDownload PDF

Published: 31 Oct 2022, Last Modified: 10 Jan 2023NeurIPS 2022 AcceptReaders: Everyone
Keywords: Interpretability, Preference Learning, Kernel, Shapley Values, RKHS
TL;DR: We propose Pref-SHAP to explain Preference Learning, even when data is not rankable
Abstract: While preference modelling is becoming one of the pillars of machine learning, the problem of preference explanation remains challenging and underexplored. In this paper, we propose \textsc{Pref-SHAP}, a Shapley value-based model explanation framework for pairwise comparison data. We derive the appropriate value functions for preference models and further extend the framework to model and explain \emph{context specific} information, such as the surface type in a tennis game. To demonstrate the utility of \textsc{Pref-SHAP}, we apply our method to a variety of synthetic and real-world datasets and show that richer and more insightful explanations can be obtained over the baseline.
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