Text Classifier Interpretation via Counterfactual and Bias Scoring
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Solution Overview
Problem
Existing text classification systems using deep neural networks are opaque and lack clear interpretation methods, making it difficult for users to understand model decisions and trust the system, especially in identifying biases and counterfactuals.
Innovation Solution
A text classification model interpreter that generates importance scores, counterfactual scores, and bias scores, allowing users to explore model behavior and understand how inputs influence predictions across various contexts, using a global learning objective and learned interpretation models to provide actionable insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural network models are used for text classification, then classification accuracy is improved, but model interpretability deteriorates
Solution Approach 1:
The patent introduces an interpretation module as an intermediary between the deep neural network classifier and the user. This module generates human-understandable explanations (such as attention weights, feature importance scores, or natural language explanations) that mediate the opaque internal representations of the neural network, allowing users to understand model decisions without sacrificing classification accuracy
Solution Approach 2:
The patent replaces the opaque mechanical processing of deep neural networks with interpretable representation methods. Instead of relying solely on black-box neural network transformations, the system substitutes or supplements them with interpretable models or explanation mechanisms that reveal decision-making processes while maintaining classification performance
2Loss of information
If post-hoc interpretation methods are used, then model transparency is improved, but user trust and actionable insights deteriorate
Solution Approach 1:
The patent segments the interpretation output into multiple distinct components (e.g., feature importance scores, attention distributions, counterfactual examples, and confidence metrics) rather than providing a single opaque score. This segmentation allows users to examine different aspects of model behavior separately, building trust through comprehensive transparency and enabling actionable insights from specific interpretable elements
Solution Approach 2:
The patent inverts the traditional approach by not only explaining what the model predicts but also what the model would predict under alternative conditions (counterfactuals). This inversion provides users with actionable insights by showing how changes in input features would affect predictions, enabling better decision-making and trust
3Productivity
If heuristic-based interpretation methods are used, then computational efficiency is improved, but reliability and testability deteriorate
Solution Approach 1:
The patent incorporates feedback mechanisms where the interpretation module continuously refines its explanations based on user interactions and model predictions. This feedback loop allows the system to learn from actual usage patterns and improve interpretation reliability over time while maintaining computational efficiency through iterative optimization rather than exhaustive computation
Solution Approach 2:
The patent changes the parameters of the interpretation system dynamically, adjusting the level of detail, computation depth, and explanation type based on input characteristics and user preferences. This allows the system to maintain computational efficiency by using simpler interpretation methods when appropriate while switching to more rigorous methods when reliability is critical
Data Source
AI summary
Techniques for interpreting a text classifier model are described. An exemplary method includes receiving a request to interpret the text classifier; receiving input text to be used to interpret the text classifier; interpreting the text classifier using the input text and masked input text to determine two or more of a counterfactual score for the received input text or an aspect thereof, an importance score for the received input text or an aspect thereof, and a bias score for the received input text or an aspect thereof as requested by the request, and providing the determined one or more scores is provided to a requester.