Explainability Vector for Downstream Model Context
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing artificial intelligence applications do not adequately transfer insights from upstream machine learning models to downstream models, particularly in handling classification edge cases and providing context for feature selection.
Innovation Solution
The use of explainability vectors from upstream machine learning models to perform principal component analysis or factor analysis, generating an encoding map to translate features, and providing these contextual features to downstream models to improve accuracy and transparency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only the output of the upstream model is provided to the downstream model, then the system complexity is low, but the prediction accuracy of the downstream model is insufficient
Solution Approach 1:
The patent introduces an explainability vector as an intermediary between the upstream model and downstream model. This vector captures the importance and correlation of input features, serving as a mediator that transfers contextual information without requiring direct access to the upstream model's internal parameters. The explainability vector acts as a bridge that enhances downstream model accuracy while maintaining system modularity.
Solution Approach 2:
The patent performs preliminary analysis by extracting an explainability vector from the upstream model before training the downstream model. This pre-computed vector, which contains feature importance scores and correlation information, is prepared in advance and used to guide the downstream model's feature selection and weight initialization, thereby improving training efficiency and final accuracy.
2Loss of information
If explainability vectors and contextual features are extracted and processed, then the contextual information for downstream models is enhanced, but the computational processing time increases
Solution Approach 1:
The patent extracts only the essential contextual information from the upstream model in the form of an explainability vector, rather than transferring the entire model or all intermediate representations. This selective extraction focuses on feature importance scores and correlation coefficients, capturing the most valuable contextual information while minimizing computational overhead.
Solution Approach 2:
The patent transforms the upstream model's internal parameters into a different representation format - the explainability vector - which expresses model behavior in terms of feature importances and correlations. This parameter transformation makes the contextual information more suitable for downstream model training while reducing the complexity of information transfer.
3Reliability
If the upstream model's internal parameters are directly transferred to the downstream model, then the feature context is preserved, but the adaptability to different downstream tasks is reduced
Solution Approach 1:
The patent segments the upstream model's knowledge into distinct components: feature importance scores and feature correlation coefficients. These segmented elements are then independently applied to the downstream model, allowing each aspect of contextual information to be utilized appropriately without forcing a one-size-fits-all parameter transfer approach.
Solution Approach 2:
Instead of directly transferring upstream model parameters to the downstream model, the patent inverts the approach by using the upstream model to generate an explainability vector that describes the relationships between features and outputs. This inverted approach provides contextual guidance without imposing the upstream model's specific parameter configurations, thereby maintaining downstream model adaptability.
Data Source
AI summary
Systems and methods for ranking user interface elements using explainability vectors. The system receives training data for a ranking machine learning model. The training data comprises values for a first set of features. The system trains the ranking machine learning model using the training data. The system processes the ranking model to extract an explainability vector. Based on the explainability vector, the system processes the first set of features to generate a second set of features. The system processes the second set of features and the output of the predictive machine learning model to generate an explanative factor and trains a ranking model using the explanative factor and a third set of features. The system receives as output from the ranking model a vector indicating display positions and rankings of user interface elements.