Black-Box ML Output Summaries for Prediction Interpretability
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Solution Overview
Problem
Software black-box machine learning models lack sufficient interpretability and explainability, leading to inefficiencies and potential misinterpretation of outputs, which can cause workflow delays and unnecessary resource usage.
Innovation Solution
A method to generate summary metadata by selecting a subset of training data objects and generating metadata based on word scores and sentence structures, using CatBoost encoder and IDF values, to provide clear reasons for model predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If software black-box machine learning models are used to generate predictive outputs, then productivity and automation are improved, but interpretability and explainability of outputs deteriorate
Solution Approach 1:
The patent introduces an intermediary explanation generation system that sits between the black-box machine learning model and the end user. This intermediary takes the model's predictive outputs and generates human-readable explanations that bridge the gap between automated decision-making and human understanding. The explanation generation component acts as a mediator that translates opaque algorithmic decisions into interpretable narratives without modifying the original black-box model's predictive functionality.
2Ease of operation
If sufficient interpretability is provided for machine learning outputs, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The patent segments the overall system into distinct functional modules: the original black-box machine learning model for predictive output generation, and a separate explanation generation system for creating interpretable narratives. This segmentation allows each component to specialize in its specific function - the model focuses on accurate predictions while the explanation system focuses on human understanding - thereby managing complexity through modular design rather than attempting to combine both functions in a single monolithic system.
Solution Approach 2:
The explanation generation system serves as an intermediary layer that adds interpretability without requiring fundamental changes to the underlying black-box model. This intermediary approach allows the system to maintain the predictive power of complex models while adding a separate, manageable layer of explanation generation that improves ease of operation without directly increasing the core model's complexity.
3Measurement precision
If manual interpretation of machine learning outputs is required, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent implements preliminary action by automatically generating explanations for machine learning outputs as part of the standard prediction process, rather than requiring subsequent manual interpretation. The explanation generation system proactively creates human-readable narratives alongside predictive outputs, so that interpretable information is already prepared and available when needed, eliminating workflow delays associated with manual analysis while maintaining interpretation accuracy.
Data Source
AI summary
Methods, apparatuses, systems, computing entities, and/or the like are provided. An example method may include receiving a data object comprising feature metadata and flag metadata generated by at least a software black-box machine learning model via processing the feature metadata associated with the data object; selecting a subset of training data objects from a plurality of training data objects associated with the software black-box machine learning model based at least in part on the feature metadata by mapping the data object into a multi-dimensional mapping space comprising mappings of the plurality of training data objects; determining a subset of note metadata corresponding to the subset of training data objects; generating summary metadata for the data object based at least in part on a plurality of word scores associated with the subset of note metadata; and causing rendering of the summary metadata on a user computing entity.