Shapley Based Residual Decomposition for Instance Analysis

TL;DR

This paper proposes a novel, model-agnostic method for analyzing the influence of individual data instances on regression residuals, enhancing interpretability and data-model fit assessment in Explainable AI.

Contribution

It introduces a Shapley-based residual decomposition approach for instance analysis, a new perspective in explainability research.

Findings

Enables identification of influential data instances.

Provides a framework for assessing model and data appropriateness.

Applicable to various explainability tasks.

Abstract

In this paper, we introduce the idea of decomposing the residuals of regression with respect to the data instances instead of features. This allows us to determine the effects of each individual instance on the model and each other, and in doing so makes for a model-agnostic method of identifying instances of interest. In doing so, we can also determine the appropriateness of the model and data in the wider context of a given study. The paper focuses on the possible applications that such a framework brings to the relatively unexplored field of instance analysis in the context of Explainable AI tasks.