Shapley Based Residual Decomposition for Instance Analysis

Individual data points can be systematically evaluated for their impact on AI model performance. The paper introduces a method to decompose model errors by instance rather than by feature, enabling researchers to identify which specific data points most influence how well a model works. This is important for understanding whether poor model performance stems from problematic training data rather than flawed model design.

The approach works across different types of AI models without modification. Because the method is "model-agnostic," it can be applied to any regression model regardless of its underlying architecture, making it a broadly applicable tool for responsible AI auditing rather than requiring custom analysis for each model type.

This framework can help identify when data or models are inappropriate for their intended use. By analyzing individual instances, the method allows researchers to determine whether a dataset and model combination are actually suitable for a particular application—a critical step in responsible AI deployment that's often overlooked.

Instance analysis represents an underdeveloped area in explainable AI that deserves more attention. The paper highlights that while feature-based explanations are common, understanding the role of individual data points has been relatively neglected, suggesting this work opens a new frontier for making AI systems more transparent and accountable.