Contextual Prediction Difference Analysis for Black-Box Model Interpretability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing model-agnostic explanation methods for black-box classification models, such as LIME and PDA, face inefficiencies and inconsistencies in explaining complex deep neural networks, particularly when dealing with high-dimensional data like images, due to the need for multiple classifications and the saturation problem where many features contribute to a single classification decision.
Innovation Solution
The proposed method, Contextual Prediction Difference Analysis (CPDA), calculates relevance values by considering the effect of contextual features instead of individual features, using image patches as input and sliding them over the entire image with a fixed stride, allowing for efficient and consistent explanations without requiring access to model parameters or extensive sampling processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If model-agnostic methods like LIME and PDA are used to explain black-box classification models, then interpretability is improved, but computation time and efficiency deteriorate due to requiring multiple classifications and extensive sampling processes
Solution Approach 1:
The patent extracts only the necessary information (input instance and output prediction) from the complex classification model, avoiding the need to access internal model parameters, architecture, or gradients. This extraction approach enables explanation generation without extensive sampling or multiple classifications, resolving the contradiction between interpretability and computation time by focusing only on the model's input-output behavior.
Solution Approach 2:
The patent segments the explanation process into identifying individual feature effects through targeted perturbations rather than requiring comprehensive sampling of the entire feature space. By segmenting the analysis to focus on individual feature contributions to the prediction, the method achieves interpretability without the computational burden of extensive sampling processes required by traditional model-agnostic methods.
2Measurement precision
If existing saliency methods are applied to understand the relationship between input and output, then feature relevance identification is improved, but reliability deteriorates because some methods are independent of both the model and the data generating process
Solution Approach 1:
The patent changes the parameter of interest from individual feature values to the effect of each feature on the prediction by systematically perturbing features and observing output changes. This parameter change enables the method to identify feature relevance in a way that is dependent on both the specific model behavior and the data characteristics, thereby improving reliability while maintaining measurement precision.
Solution Approach 2:
The patent incorporates feedback by observing how changes in individual features affect the model's prediction output. By using the prediction changes as feedback to determine feature effects, the method ensures that the explanation is grounded in the actual model behavior and data characteristics, improving reliability compared to methods that generate explanations independent of the specific model and data.
3Measurement precision
If PDA requires many time classifications for each feature to observe output changes, then feature effect measurement is improved, but productivity deteriorates when input dimensions are very high such as images
Solution Approach 1:
The patent applies partial action by performing classifications only for the specific perturbations needed to measure individual feature effects, rather than requiring comprehensive multiple classifications for each feature. This selective approach maintains measurement precision for feature effects while dramatically reducing the total number of classifications required, thereby improving productivity for high-dimensional inputs like images.
4Loss of information
If LIME trains a new classifier for each classification to approximate the original model, then local interpretability is improved, but efficiency deteriorates and explanations become inconsistent across different classifiers
Solution Approach 1:
The patent enables the classification model to explain its own decisions by directly analyzing the relationship between its inputs and outputs without requiring external interpretable classifiers. This self-service approach eliminates the need to train separate classifiers for each explanation, improving efficiency while maintaining consistent explanations based on the original model's actual behavior rather than approximations.
Data Source
AI summary
Provided is a method and processing unit for computer-implemented analysis of a classification model which is adapted to map, as a prediction, a number of input instances, each of them having a number n of features, into a number of probabilities of output classes, as a classification decision, according to a predetermined function, and which is adapted to determine a relevance value for each feature resulting in a saliency map. The disclosure includes the step of identifying an effect of each feature on the prediction of the instance by determining, for each feature, a relevance information representing a contextual information for all features of the instance omitting the considered feature. Then, the relevance value for each feature is determined. Finally, the plurality of relevance values for the features of the instance is evaluated to identify the effect of each feature on the prediction of the instance.