Explainability Engine for Black Box ML Transparency
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Solution Overview
Problem
Machine learning systems, such as neural networks and language models, operate as 'black boxes' lacking transparency, making it difficult to detect biases and identify important features, which hinders model accuracy and efficiency.
Innovation Solution
An explainability engine is implemented to provide natural language explanations for outcomes generated by machine learning models, using a bootstrapping approach to tune language models and reduce unnecessary features, enhancing model interpretability and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning systems use thousands, millions, or billions of trainable parameters to improve accuracy, then prediction accuracy is improved, but transparency and interpretability deteriorate
Solution Approach 1:
The patent introduces an explainability engine as an intermediary component that sits between the complex machine learning model and the user. This engine generates natural language explanations that translate the model's internal decision-making processes into human-understandable formats, thereby maintaining high accuracy while improving transparency without modifying the core model architecture.
Solution Approach 2:
The patent replaces the need for direct human interpretation of complex model parameters with an automated explanation generation system. Instead of requiring users to understand thousands or millions of parameters mechanically, the system substitutes this with natural language generation that conveys the same information in an interpretable format.
2Ease of operation
If machine learning systems operate as black boxes to maintain simplicity, then ease of operation is improved, but ability to detect biases and identify important features deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the explainability engine continuously generates explanations for model predictions. These explanations provide feedback about which features are important and how decisions are made, enabling users to detect biases and understand model behavior without complicating the core system operation.
Solution Approach 2:
The explainability engine serves as a mediator that bridges the gap between simple black box operation and transparent decision-making. It allows the system to maintain operational simplicity while providing the necessary transparency for bias detection and feature identification through generated explanations.
3Reliability
If unnecessary features are kept in training datasets, then model training completeness is improved, but training efficiency and speed deteriorate
Solution Approach 1:
The patent extracts and removes unnecessary features from training datasets using the explainability engine to identify which features are actually important for predictions. By taking out redundant features, the system maintains training completeness for essential features while improving training efficiency by reducing the total feature space that needs to be processed.
Solution Approach 2:
The patent applies partial action by selectively including only the necessary features in training rather than processing all available features. This approach achieves sufficient model training completeness without the excessive computational burden of processing unnecessary features, thereby improving training speed.
4Productivity
If model parameters are reduced to improve efficiency, then training efficiency is improved, but model accuracy may deteriorate
Solution Approach 1:
The patent extracts and removes redundant model parameters while retaining those that are critical for accuracy. The explainability engine identifies which parameters contribute most to predictions, allowing the system to reduce parameter count for efficiency while maintaining the essential parameters needed for accurate predictions.
Solution Approach 2:
The patent changes the parameter space by reducing the number of parameters through feature selection and model simplification. By carefully selecting which parameters to retain based on their importance to prediction accuracy, the system achieves better training efficiency without significant accuracy deterioration.
Data Source
AI summary
The subject technology uses a bootstrapping approach to train language models (LMs) to explain outcomes determined by neural networks, ensemble models, reinforcement learning models, LMs, and other black box machine learning models. The bootstrapping approach may train multiple iterations of a tuned LM using training data determined from progressively complex machine learning models and progressively detailed natural language explanations. The model explanations determined by the tuned LM may be displayed in a user interface (UI) included in a publishing system to provide users more context about and a greater understanding of the decision making process used by the black box machine learning models to determine outcomes.