Linear Programming Approximation for Interpretable ML Visualization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine learning models, especially black box models, are difficult to interpret, leading to distrust in critical applications like self-driving cars and disease diagnosis, as they can echo biases in the data and their decision-making processes are convoluted, necessitating improved visualization techniques to build trust and understand their decisions.
Innovation Solution
A model-agnostic visualization method using linear programming approximations that perturbs language elements by removing subsets to analyze changes in classifier outcomes, providing a visual representation of importance and employing faithfulness metrics to ensure accurate portrayal of classifier decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are used to improve prediction accuracy, then productivity and decision quality are improved, but the models become black boxes that are difficult to interpret, leading to loss of trust and reliability issues
Solution Approach 1:
The patent introduces linear programming approximations as an intermediary layer between the black box classifier and the user. This intermediary model approximates the complex classifier's behavior using simple linear relationships, making the decision-making process interpretable while maintaining prediction accuracy. The visualization system acts as a mediator that translates complex model decisions into understandable visual representations.
Solution Approach 2:
The patent creates simplified copies or approximations of the complex classifier's decision boundaries using linear programming. These approximations capture the essential decision-making logic in a interpretable form, allowing users to understand model behavior without dealing with the complexity of the original black box model.
2Productivity
If complex black box models are deployed to handle difficult classification tasks, then prediction capability is improved, but understanding the decision-making process becomes convoluted, leading to loss of information about model reasoning
Solution Approach 1:
The patent segments the complex classification process into multiple interpretable linear approximations. By dividing the decision space into regions governed by simple linear relationships, the system maintains classification capability while making each decision step transparent and understandable.
Solution Approach 2:
The visualization system serves as an intermediary that captures and preserves information about the model's reasoning process. It translates the black box decisions into visual representations that retain information about which features influenced decisions and how, preventing information loss.
3Reliability
If perturbation methods are used to analyze classifier decisions, then model interpretability is improved, but computational complexity increases due to multiple linear programming approximations
Solution Approach 1:
The patent changes the parameter representation from complex non-linear model parameters to simple linear programming parameters. This transformation maintains interpretability while reducing computational complexity, as linear programming is more efficient and easier to interpret than analyzing complex neural network parameters.
Data Source
AI summary
A method of determining influence of language elements in script to an overall classification of the script by perturbing the dataset representing a conversation. In some instances, for example, in the 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.