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Attribution method that integrates gradients along a path from a baseline to the input to assign feature importance for a model’s prediction.
DESCRIPTION
Integrated Gradients explains predictions by accumulating gradients from a baseline to the input, satisfying sensitivity and implementation invariance axioms. It is efficient for deep networks and yields smooth attributions. Baseline choice critically affects results; common baselines include zeros or blurred inputs. Implementations exist in Captum and TensorFlow tutorial code. IG is used for compliance, debugging, and fairness analysis.
ALTERNATIVE DEFINITIONS
Sundararajan, Taly, Yan (arXiv)
Integrated gradients are defined as the path integral of the gradients of the output with respect to the input along a straight line from a baseline to the input.
Provides formal mathematical definition via path integral.
TensorFlow
Integrated gradients are a technique for attributing a model’s prediction to its input features.
Emphasizes practical attribution use for deep models.
Captum
Integrated Gradients is an attribution method that assigns importance to input features by accumulating gradients along a path from baseline to input.
Highlights implementation details and baseline-path accumulation.
RELATED TERMS
Saliency Map Feature Importance Explainability Fairness Baseline Selection Gradient-based Attribution Model Cards
COMPETING TERMS
SHAP Values LIME DeepLIFT
KNOWLEDGE AREAS
AI Data Science MLOps Testing & Validation
TARGET AUDIENCE
Data Scientist AI Engineer MLOps Engineer Chief AI Officer
EXTERNAL SOURCES
- Wikipedia - Explainable artificial intelligence
- TensorFlow - Hands-on IG tutorial
- Captum - PyTorch IG implementation
- arXiv - Original IG method paper
TOP QUESTIONS
Why use Integrated Gradients for explainability?
It satisfies key axioms and is efficient for deep networks, providing reliable feature attributions.
Category: Relevancy
How do you choose a baseline in Integrated Gradients?
Use a neutral input (e.g., zeros, blurred image) that represents absence of features; validate sensitivity to baseline.
Category: Requirements
What’s a common mistake with Integrated Gradients?
Selecting an unrealistic baseline, which skews attributions and interpretation.
Category: Mistakes
How is Integrated Gradients computed?
Approximate the path integral by summing gradients over discrete steps from baseline to input.
Category: HowTo
How does IG compare to SHAP and LIME?
IG relies on model gradients; SHAP uses Shapley values, LIME perturbs inputs locally. Each has trade-offs.
Category: Alternatives
What risks exist when using Integrated Gradients?
Misinterpretation of attributions and overreliance without robustness checks can lead to faulty conclusions.
Category: Risks
Where is Integrated Gradients used?
Healthcare, finance, and regulated domains for auditing models and building trust.
Category: Example
How to validate Integrated Gradients explanations?
Perform sanity checks, compare across baselines, and run invariance/sensitivity tests.
Category: Testing
What’s hot around Integrated Gradients today?
Path choice variants, noise smoothing, and combining IG with concept-based explanations.
Category: Topic