The Neglected Baseline in Model Interpretation

2026-06-30 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

This work emphasizes the long-overlooked baseline issue in model interpretation, arguing that its neglect often leads to imprecise or incorrect results. The authors reformulate model interpretation tasks and principles to highlight the baseline's importance, unifying gradient-based methods, Integrated Gradients (IG), and Taylor expansion while explicitly identifying baselines for each. They analyze flaws in existing methods like IG, LayerCAM, ODAM, and Difference Map, advocating for evaluating interpretation quality through attribution error rather than flawed marginal-effect or perfect model performance assumptions. The paper introduces a revised IG method with a clear, reasonable baseline, achieving better results and supporting interpretation based on features from any layer, demonstrating that differences reflect varying degrees of feature extraction.

Key takeaway

For AI scientists and ML engineers developing or evaluating model interpretation methods, recognize that a clearly defined input baseline is critical for accurate results. Traditional evaluation metrics like marginal-effect are flawed; instead, adopt attribution error to rigorously assess interpretation quality. This approach, exemplified by the revised Integrated Gradients, ensures more reliable insights into model decisions, especially across different feature layers, guiding more effective model optimization.

Key insights

Neglecting baselines in model interpretation leads to imprecise results; a clear baseline and attribution error are crucial for accuracy.

Principles

• Model interpretation requires a clear, explicitly defined baseline.
• Attribution error is a precise metric for evaluating interpretation quality.
• The input should solely determine the interpretation's baseline.

Method

Reformulate interpretation tasks and principles, unify gradient-based, IG, and Taylor expansion methods, then revise IG with a clear input-determined baseline and evaluate using attribution error.

In practice

• Use an all-zero input as a common baseline for image interpretation.
• Evaluate model interpretation methods using attribution error, not marginal-effect.
• Interpret features from any model layer; differences reflect extraction stages.

Topics

• Model Interpretation
• Integrated Gradients
• Explainable AI
• Attribution Error
• Deep Learning
• Object Detection
• Baseline

Code references

• Cyang-Zhao/ODAM

Best for: Research Scientist, AI Engineer, NLP Engineer, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.