Entity Data Segmentation with Boolean Friction Point Explanations
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Solution Overview
Problem
Existing machine learning solutions provide either global explainability that is too non-granular or local explainability that is too complex and unactionable, making it difficult to mitigate undesirable predictions effectively.
Innovation Solution
A system and method that segments data entities based on Boolean friction points using Shapley values to generate actionable global explanations for each segment, highlighting the top N most significant friction points and features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If local explainability is provided per instance, then explanation granularity is improved, but the quantity of instances becomes too large to address effectively and attributes become unactionable
Solution Approach 1:
The patent segments the dataset into multiple groups based on Shapley values and Boolean friction points, creating manageable subsets that can be analyzed collectively. This segmentation reduces the overwhelming quantity of individual instances while preserving actionable insights, allowing businesses to address explanations in organized batches rather than individually.
Solution Approach 2:
The patent transforms continuous Shapley values into discrete Boolean friction points (true/false categories). This parameter transformation simplifies the data structure and makes attributes more actionable for business users, converting complex numerical explanations into clear categorical insights that drive mitigation strategies.
2Ease of operation
If global explainability is provided, then statistical overview is improved, but granularity is insufficient for practical mitigation
Solution Approach 1:
The patent divides the global dataset into segments based on Shapley value thresholds and Boolean friction points. This creates hierarchical explainability where global statistics provide overview while segment-level details provide granularity, enabling both high-level understanding and specific actionable insights without requiring separate analyses.
Solution Approach 2:
The patent introduces Shapley values as an intermediary mechanism that bridges global explainability and local instance-level details. Shapley values serve as a mediating layer that aggregates individual instance contributions while maintaining enough detail to identify actionable patterns, enabling practical mitigation strategies.
3Measurement precision
If local explainability attributes are provided, then instance-level detail is improved, but actionable attributes become masked and complicated
Solution Approach 1:
The patent transforms complex continuous Shapley values into discrete Boolean friction points (true/false categories). This parameter transformation simplifies the data structure and makes attributes more actionable for business users, converting complex numerical explanations into clear categorical insights that drive mitigation strategies.
Solution Approach 2:
The patent extracts and highlights only the most significant friction points and attributes from the full set of local explainability data. By identifying and separating the critical actionable attributes from less relevant ones, the patent makes the explanation framework clearer and more focused on what businesses can actually act upon.
Data Source
AI summary
As described herein, a system, method, and computer program provide explainability of entity data segmentation based on Boolean friction points. A dataset is processed, using a machine learning model, to calculate a plurality of Shapley values for the dataset, wherein the dataset includes friction points and explanatory variables. The dataset is clustered to generate a plurality of segments, based on the Shapley values. For each segment of the plurality of segments, a global explanation is generated for the segment using a predefined list of Boolean friction columns and the Shapley values.