Explainability System for Machine Learning Feature Impact Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning models in the financial services industry operate as 'black boxes,' lacking transparency regarding the importance and impact of input features on their operations and outputs, which hinders understanding and potential biases.
Innovation Solution
A computer-implemented system that dynamically analyzes and monitors machine learning models by generating modified feature vectors and applying machine learning processes to them, providing explainability data on the impact of input features through graphical representations within a web-based interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are used to improve decision-making in financial services, then productivity and accuracy of decisions are improved, but transparency and understandability of model operations deteriorate
Solution Approach 1:
The patent introduces an explainability system as an intermediary between the machine learning model and users. This system generates explainability data that translates complex model operations into understandable information, allowing users to comprehend model decisions without sacrificing the model's predictive power or efficiency
Solution Approach 2:
The patent segments the machine learning model into analyzable components by generating explainability data for individual features and their contributions. This segmentation allows users to understand the specific impact of each input feature on model outputs, transforming the opaque black box into a series of interpretable elements
2Measurement precision
If complex machine learning models are deployed to improve prediction accuracy, then measurement precision is improved, but ease of operation and monitoring deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where the explainability system continuously generates and provides information about model operations and feature contributions. This feedback loop enables operators to monitor model behavior, understand prediction rationale, and maintain control over complex models without reducing their accuracy
3Device complexity
If machine learning models operate as black boxes to maintain simplicity, then device complexity is reduced, but ability to detect biases and understand feature impact deteriorates
Solution Approach 1:
The explainability system serves as an intermediary layer that adds analytical capabilities without modifying the underlying machine learning model structure. This approach maintains model simplicity while enabling comprehensive detection and measurement of feature impacts and potential biases
Data Source
AI summary
The disclosed embodiments include computer-implemented processes that flexibly and dynamically analyze a machine learning process, and that generate analytical output characterizing an operation of the machine learning process across multiple analytical periods. For example, an apparatus may receive an identifier of a dataset associated with the machine learning process and feature data that specifies an input feature of the machine learning process. The apparatus may access at least a portion of the dataset based on the received identifier, and obtain, from the accessed portion of the dataset, a feature vector associated with the machine learning process. The apparatus may generate a plurality of modified feature vectors based on the obtained feature vector, and based on an application of the machine learning process to the obtained and modified feature vectors, generate and transmit, to a device, first explainability data associated with the specified input feature for presentation within a digital interface.