Introduction to AI Explanations for AI Platform

AI Explanations is a feature integrated into Google Cloud's AI Platform Prediction that helps users understand their model's outputs for classification and regression tasks. It provides feature attributions, which indicate how much each feature in the model contributed to the predictions for each given instance[1][2].

Key Features and Benefits

1. Feature Attributions: When you request explanations, you receive predictions along with feature attribution information, showing how each feature affected the prediction relative to a specified baseline[1][2].

2. Visualization Capabilities: AI Explanations works on tabular data and includes built-in visualization for image data, such as overlays showing which pixels or areas contributed most to a prediction[1][2].

3. Debugging and Optimization: Feature attributions can help detect data issues that standard evaluation techniques might miss and identify less important features for model optimization[1][2].

Feature Attribution Methods

AI Explanations offers three methods for feature attributions:

1. Sampled Shapley:

- Recommended for non-differentiable models (e.g., ensembles of trees and neural networks)

- Assigns credit to each feature by considering different feature permutations

- Provides a sampling approximation of exact Shapley values[1][2]

2. Integrated Gradients:

- Recommended for differentiable models (e.g., neural networks)

- Calculates the gradient of the prediction output with respect to input features along an integral path

- Efficient for models with large feature spaces and low-contrast images[1][2]

3. XRAI (eXplanation with Ranked Area Integrals):

- Recommended for models accepting image inputs, especially natural images

- Based on the integrated gradients method

- Assesses overlapping image regions to create a saliency map highlighting relevant areas[1][2]

Limitations

1. Attributions are specific to individual predictions and may not be generalizable to entire classes or models[1][2].

2. It may be unclear whether issues arise from the model or the training data[1][2].

3. Feature attributions can be subject to adversarial attacks, similar to predictions in complex models[1][2].

Underlying Concept

All feature attribution methods are based on Shapley values, a cooperative game theory algorithm that assigns credit to each "player" (feature) for a particular outcome (prediction)[1][2].

By providing these explanations, AI Explanations helps users verify model behavior, recognize bias, and identify potential improvements for both models and training data[1][2]

Citations:

[1] https://cloud.google.com/ai-platform/prediction/docs/ai-explanations/overview

[2] https://cloud.google.com/ai-platform/prediction/docs/ai-explanations/overview