AI Analysis Report
Examining Baseline Effects on Explainability Metrics
This paper highlights a critical problem in evaluating Explainable AI (XAI) attribution methods: the choice of baseline function significantly impacts the reliability and ranking of these methods, leading to inconsistent results. It proposes a novel model-dependent baseline leveraging feature visualization to achieve better information removal without generating out-of-distribution (OOD) images.
Current XAI faithfulness metrics like Insertion and Deletion, vital for understanding model behavior, are highly sensitive to the choice of baseline. Even simple linear models yield different optimal attribution methods depending on the baseline (e.g., zero-inpainting vs. uniform noise). This instability is linked to baselines failing to simultaneously remove information efficiently and remain in-distribution. We introduce a model-dependent, image-independent baseline derived from feature visualization that offers a superior trade-off, ensuring information removal while keeping generated images within the model's learned distribution, thus improving metric reliability.
Projected Impact Score
Avg. Efficiency Gain
ROI Potential (Annual)
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Understanding the core instability of XAI evaluation metrics.
80%
of Insertion score occurs in OOD regime, making it unreliable.
| Metric | Stability | OOD Sensitivity |
|---|---|---|
| Deletion | Highly dependent on baseline choice (unstable ranking). | Most score in non-OOD regime. |
| Insertion | Appears stable, but largely computed on OOD data (hides flaws). | 80% of score in OOD regime. |
The paper demonstrates that even with a simple linear model, the optimal attribution method for Deletion and Insertion can vary significantly depending on the chosen baseline. For example, using a 'zero' baseline favors Gradient-Input and Occlusion, while a 'uniform noise' baseline favors Saliency and SmoothGrad. This theoretical inconsistency highlights the fundamental problem with current baseline choices.
Defining properties for effective baselines and observed challenges.
Two key desiderata for a good baseline are identified: (i) it should remove as much semantic information as possible from the target region, and (ii) it should keep the input as much In-Distribution (ID) as possible. Current baselines exhibit an inherent trade-off, struggling to achieve both simultaneously. Baselines that remove information efficiently often push images into an Out-of-Distribution (OOD) state, and vice-versa.
Enterprise Process Flow
High Information Removal
→
Increased OOD Risk
→
In-Distribution (ID) Image
→
Less Information Removed
No current baseline
satisfies both information removal and in-distribution criteria.
Introducing a novel approach for a more reliable baseline.
The proposed solution leverages recent advancements in Feature Visualization to create a model-dependent and image-independent baseline. This approach reframes the original feature visualization objective to generate images that activate zero features in the model's penultimate layer. This ensures that semantic information is effectively removed while keeping the generated images within the model's learned distribution, thus overcoming the trade-off faced by existing baselines.
Feature Visualization for Baseline Generation
By minimizing the activations of the penultimate layer to zero, we construct images that the model perceives as 'empty' of semantic information. These baselines are inherently model-dependent (as different models learn different features) and image-independent (not relying on context from the original image). Examples across VGG-19, ResNet50, EfficientNetB0, ViT, DeiT3, and PoolFormer show vastly different 'zero-information' images, demonstrating this model specificity and improved OOD properties.
Model-dependent & Image-independent
New baseline properties for improved reliability.
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Projected Annual Savings
$5,000,000
Annual Hours Reclaimed
100,000
Your AI Implementation Roadmap
A typical enterprise-grade AI integration follows a structured approach to ensure success and measurable outcomes.
Phase 1: Initial Assessment & Baseline Customization
Evaluate current XAI practices, identify critical metrics, and generate custom model-dependent baselines using feature visualization techniques for your specific models.
Phase 2: Re-evaluation of Attribution Methods
Apply the new, robust baselines to re-evaluate and re-rank attribution methods, gaining a more reliable understanding of their faithfulness and local behavior.
Phase 3: Integration into MLOps & Monitoring
Integrate the refined evaluation framework into your MLOps pipeline to continuously monitor XAI metric reliability and adapt to model updates, ensuring consistent interpretability.
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