Explainable Neural Classification With Saliency-Guided Justification
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Solution Overview
Problem
Machine learning classifiers, particularly those based on deep learning, lack transparency and consistency in explaining their decision-making processes, leading to difficulties in assessing how and why they arrive at certain choices, which is crucial for building trust and gaining insights into their functioning.
Innovation Solution
A neural architecture for explainable classification (XCLS) that integrates a discriminator and a generator with shared weights, using attention-weighted Sequence of Embedding Vectors (SoEV) to co-train for security classification and natural language explanation, ensuring alignment and generating a discriminator verdict with a corresponding natural language explanation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning classifiers based on deep learning are used, then classification accuracy is improved, but transparency and consistency in explaining decisions deteriorates
Solution Approach 1:
The patent introduces an explanation generator as an intermediary component that receives classifier decisions and generates human-readable explanations. This mediator bridges the gap between the opaque deep learning classifier and human users, providing transparency without compromising the classifier's accuracy. The explanation generator acts as a separate module that translates complex model decisions into understandable formats.
Solution Approach 2:
The system segments the classification task into two independent parts: the classifier that performs accurate classification and the explanation generator that provides transparent reasoning. By separating these functions, the patent allows each component to optimize for its specific goal - accuracy for the classifier and transparency for the explanation generator - without compromising the other.
2Reliability
If deep learning models are used for classification, then classification power is improved, but consistency in explaining decisions deteriorates
Solution Approach 1:
The explanation generator incorporates feedback mechanisms that monitor and adjust explanation generation based on classifier decisions. This feedback loop ensures consistency in explanations by aligning them with the underlying classification logic, while the generator maintains the reliability of the classification power through continuous coordination with the classifier.
3Loss of information
If separate classifier and explanation generator are used, then transparency is improved, but device complexity increases
Solution Approach 1:
The explanation generator is designed with multi-functionality, serving both as an explanation provider and as a coordinator with the classifier. This universal component handles multiple tasks including explanation generation, consistency checking, and alignment with classification decisions, thereby reducing the need for additional separate components and managing system complexity.
Data Source
AI summary
Techniques for a neural architecture for explainable classification (XCLS) with natural language justification and explicit saliency detection are disclosed. In some embodiments, a system/process/computer program product for a neural architecture for XCLS with natural language justification and explicit saliency detection includes generating a classifier (e.g., a neural network that co-trains the discriminator for security classification and the generator for natural language explanation) that is applied to perform the following: (1) force an explicit selection of salient input regions and (2) co-train a discriminator for security classification and a generator for natural language explanation with shared weights; applying the discriminator, the generator (e.g., an LLM decoder), and attention losses to jointly learn to up-weight a salient subset of spatial input regions to ensure alignment; and generating a discriminator verdict using the classifier based on this bottlenecked information, and using the generator to output a natural language explanation of the discriminator verdict.