Gradient-Based Perturbation for Interpreting Machine Learning Classifications
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Solution Overview
Problem
Machine learning systems, particularly 'black box' models like random forests and deep neural networks, make it difficult for users to interpret outputs, especially for data points near classification boundaries, as users lack access to algorithm details and struggle to understand why certain inputs generate specific outputs.
Innovation Solution
A system that obtains output data with classifications and scores from a remote machine learning system, computes an objective function and gradient, and perturbs input data based on the gradient until the gradient is no longer significant, allowing visualization of how input data affects classifications, thereby facilitating user understanding of classification processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a black box machine learning system is used, then classification accuracy and productivity are improved, but interpretability and ease of operation deteriorate
Solution Approach 1:
The patent introduces gradient computation and perturbation analysis as intermediary tools between the black box machine learning system and the user. These intermediaries translate the opaque internal workings into visualizable transformations of input data, allowing users to understand classification decisions without accessing the complex algorithm details. The gradient-based perturbation process serves as a mediator that reveals the relationship between input features and classification outcomes.
Solution Approach 2:
The system changes the parameters of input data through controlled perturbations based on gradient information. By systematically modifying input parameters and observing the resulting changes in classification confidence, the system makes the black box behavior transparent. This parameter change approach allows users to see how small modifications to input data affect the classification, thereby improving interpretability while maintaining the original classification accuracy.
2Ease of operation
If gradient-based perturbation is applied to interpret classification, then interpretability is improved, but computational complexity and time increase
Solution Approach 1:
Instead of analyzing all possible perturbations or performing exhaustive searches through the input space, the patent applies gradient-based perturbation in the direction of maximum change. This partial action approach focuses computational resources on the most informative directions, achieving interpretability with reduced computational complexity. The method performs only enough perturbation analysis to reveal the key relationships between input data and classification, rather than exhaustively exploring all possibilities.
3Ease of operation
If data points near classification boundaries are analyzed, then interpretability of ambiguous cases is improved, but measurement precision requirements increase
Solution Approach 1:
The patent performs preliminary gradient computation and perturbation analysis specifically for data points near classification boundaries before final interpretation. By pre-identifying boundary cases and applying targeted perturbation analysis to these specific points, the system prepares interpretability information in advance. This preliminary action allows the system to handle boundary cases with appropriate precision requirements, rather than requiring high precision for all data points uniformly.
Data Source
AI summary
One embodiment provides a system for facilitating visualization of input data classifications. The system obtains, from a remote computer system, output data which includes a classification and a score that a corresponding set of input data belongs to one or more classifications. The system computes an objective function based on the score and the input data, and computes a gradient of the objective function based on the input data. In response to determining that the computed gradient is not greater than a predetermined threshold, the system perturbs the input data in a direction of or opposite to the computed gradient, and transmits the perturbed input data to the remote computer system to obtain new output data. The system displays on a device a visualization of whether the input data belongs more or less strongly to a classification.