ML Risk Score Introspection via Feature Clustering
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Solution Overview
Problem
Current machine learning approaches, such as random forests, struggle to provide transparent decision-making processes, making it difficult to understand why certain risk scores are assigned to data objects, leading to inaccuracies and gaps in model training.
Innovation Solution
Implementing an introspection technique that visualizes document features and relationships using a graph-based approach, allowing users to identify inaccurate risk scores and retrain the model by adjusting feature weights and selections, thereby providing human-readable explanations for decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models (such as random forests) are used to determine risk scores, then accuracy in risk assessment is improved, but transparency and understandability of decision-making deteriorate
Solution Approach 1:
The patent introduces an introspection interface as an intermediary between the machine learning model and users. This interface visualizes the decision-making process by displaying feature contributions, node traversal paths, and importance scores, allowing users to understand how risk scores are determined without sacrificing model accuracy
Solution Approach 2:
The patent replaces the opaque mechanical decision-making process of traditional machine learning models with a visual representation system. By substituting the black-box algorithmic process with graphical visualizations of feature contributions and decision paths, the system maintains computational accuracy while enhancing human understandability
2Productivity
If machine learning models are used for risk assessment, then processing efficiency is improved, but ability to identify and correct inaccuracies deteriorates
Solution Approach 1:
The patent implements a feedback mechanism through the introspection interface that allows users to examine feature contributions and decision paths. This feedback enables users to identify where the model may be making incorrect decisions by analyzing which features are driving predictions, thereby detecting and correcting inaccuracies while maintaining processing efficiency
3Extent of automation
If traditional machine learning approaches are used, then automation level is improved, but ease of model improvement and retraining deteriorates
Solution Approach 1:
The patent performs preliminary action by visualizing feature contributions and decision paths before model retraining. The introspection interface identifies problematic features and decision patterns that need improvement, allowing practitioners to prepare targeted retraining strategies and select appropriate training data, thereby making the automated model improvement process more manageable and effective
Data Source
AI summary
A machine learning algorithm, such as a random forest regressor, can be trained using a set of annotated data objects to estimate the risk or business value for an object. The feature contributions for each data object can be analyzed and a representation generated that clusters data objects by feature contributions. Any clustering of data objects with incorrect scores in the visualization can be indicative of gaps in the regressor training. Adjustments to the inputs can be made, and the regressor retrained, to eliminate clustering of errors for similar feature contributions. Correcting the risk score estimations can ensure that the appropriate security policies and permissions are applied to each data object.