Exploring Local Explanations of Nonlinear Models Using Animated Linear Projections

Local explanations have blind spots when variables are correlated: The paper demonstrates that standard local explanation methods (LVAs) don't effectively account for relationships between predictors, which can lead to incomplete or misleading insights into why a model made a particular decision. Using animated visualizations to explore these interactions reveals a more complete picture of model behavior.

Animation helps detect when AI models make mistakes: By converting local explanations into visual projections that can be animated and explored interactively, researchers can better understand not just correct predictions, but also identify why a model failed—information that's crucial for catching errors before deployment in high-stakes applications.

Outliers and data clustering affect model explanations: The paper shows that the quality and reliability of local explanations varies depending on whether an observation is typical or unusual, and how observations cluster together. This suggests that explanations should be interpreted differently depending on where a data point falls in the broader dataset.

Visualization makes complex model behavior more accessible: Rather than relying solely on numerical summaries, the animated linear projection approach (implemented in the open-source R package cheem) makes it possible for non-specialists to visually explore how machine learning models use multiple interacting variables—supporting more informed oversight of AI systems.