Machine Learning Opacity via Replicator and Translator Modules
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Solution Overview
Problem
Complex machine learning applications suffer from opaqueness, making it difficult to understand, interpret, or trace the input to output processes, which hinders improvements and deployments in technical computing environments.
Innovation Solution
A computing system comprising a replicator module, a translator module, and a graphical user interface is employed to pierce the black box effect of complex machine learning applications. The replicator module uses local and global effect modeling to create a replicated semi-additive index data structure, while the translator module generates explanatory mappings of inputs to outputs, and the graphical user interface renders selected characteristics of the data structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex machine learning applications are used to improve predictive performance and flexibility of modeling, then analysis and predictive performance are improved, but input to output traceability deteriorates leading to opaqueness
Solution Approach 1:
The patent introduces an intermediary system comprising a replicator module, translator module, and graphical user interface that acts as a mediator between the complex machine learning application and users. The replicator module creates simplified replicas of complex models, the translator module converts complex model operations into interpretable explanations, and the GUI presents these explanations to users, thereby restoring traceability without sacrificing predictive performance
Solution Approach 2:
The replicator module creates simplified copies or replicas of complex machine learning models that preserve key input-output relationships while being more interpretable. These replicas serve as transparent alternatives that maintain predictive accuracy while enabling traceability and understanding of model behavior
2Adaptability or versatility
If the complexity of machine learning applications increases to handle Big Data volumes and speeds, then flexibility of modeling and data structure creation are improved, but understanding and interpretation of computing results deteriorates
Solution Approach 1:
The patent segments the complex machine learning system into distinct components: the original complex model, the replicator module that creates simplified versions, the translator module that generates explanations, and the GUI that presents results. This segmentation allows the system to maintain complex modeling capabilities while providing simplified interpretable outputs for understanding
Solution Approach 2:
The system applies different qualities to different parts of the overall system: the complex machine learning models maintain high complexity for handling Big Data and flexible modeling, while the replicator and translator modules provide locally simplified, interpretable representations that are easy to understand and interpret
Data Source
AI summary
Computing systems and technical methods that transform data structures and pierce opacity difficulties associated with complex machine learning algorithms are disclosed. Advances include a framework and techniques that include: i) global diagnostics; ii) locally interpretable models LIME-SUP-R and LIME-SUP-D; and iii) explainable neural networks.