Model-Agnostic Visualization via Linear Programming Approximation
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Solution Overview
Problem
Machine learning models, particularly black box models, are difficult to interpret, leading to distrust in applications where model predictions significantly impact human lives, as they can echo biases and lack transparency in decision-making processes.
Innovation Solution
A model-agnostic visualization method using linear programming approximations that perturbs input elements, such as sentences or turns in conversations, to determine their importance in classification outcomes, providing a visual representation of the classifier's decisions and a faithfulness metric to ensure accurate portrayal of the classifier's behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to improve prediction accuracy, then classification performance is improved, but model interpretability deteriorates leading to distrust
Solution Approach 1:
The patent introduces an intermediary visualization system that acts as a mediator between the black-box classifier and human users. This system perturbs input data systematically and visualizes the effects, providing an intermediate layer of interpretation that maintains the high accuracy of complex models while making their decisions understandable to users through visual representations of input perturbations and their impact on predictions
2Adaptability or versatility
If complex black box models are deployed to handle sophisticated classification tasks, then prediction capability is improved, but transparency in decision-making deteriorates
Solution Approach 1:
The patent segments the decision-making process into visualizable components by systematically perturbing individual input features and observing their impact on the model's prediction. This segmentation allows the complex decision process of black-box models to be broken down into discrete, interpretable segments that can be visualized and understood, maintaining prediction capability while recovering lost transparency information
3Reliability
If model interpretation methods are developed to improve trust, then user confidence is improved, but computational complexity increases
Solution Approach 1:
The patent implements a self-service interpretation approach where the system automatically generates visual explanations by perturbing its own input data and analyzing the effects on predictions. This self-service mechanism provides trust-building visualizations without requiring external complex interpretation tools, improving user trust while keeping the overall system complexity manageable through automated self-explanation
Data Source
AI summary
A method of determining influence of language elements in script to an overall classification of the script includes performing a sentiment analysis of the language elements. In some instances, for example, in a conversation, the language elements and turns within the conversation (e.g., in a chat bot) are analyzed for their influence in escalation or non-escalation of the conversation to a higher level of resolution, e.g., to a human representative or manager.