Grid-Based Model Explanation for Bias and Coverage Analysis
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Solution Overview
Problem
Large dimensional modeling datasets are complex and often lead to biased models due to unnoticed biases and unexplained decision-making, making it difficult to ensure accuracy and robustness, especially in applications like credit risk assessment and cyber threat detection.
Innovation Solution
A method and apparatus for analyzing large dimensional datasets by discretizing features into bins, creating a grid representation, and using grid indexing and storage to simplify and visualize data distribution, identify biases, and explain model decisions, employing a framework that is agnostic to model architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional sampling methods are used to analyze large datasets, then analysis complexity is reduced, but important data points and biases may be missed
Solution Approach 1:
The patent segments the large dataset into multiple partitions or blocks, allowing analysis of each partition separately while maintaining coverage of the entire dataset. This avoids the information loss of sampling while reducing the complexity of analyzing all data points in one go, as each partition can be processed independently.
2Reliability
If machine learning models with complex architecture are used, then model performance improves, but interpretability and understanding of decision basis deteriorates
Solution Approach 1:
The patent introduces an intermediary analysis layer that examines the relationship between input features and model outputs without requiring changes to the complex model architecture. This intermediary analysis provides interpretability by identifying important features and their relationships, acting as a mediator between the complex model and the user's need for understanding.
3Loss of information
If datasets with many dimensions are analyzed, then coverage and completeness improve, but analysis complexity and computational requirements increase
Solution Approach 1:
The patent transforms the multi-dimensional data analysis problem by introducing a new dimensional perspective through partitioning and selective feature examination. Instead of attempting to visualize or directly analyze all dimensions simultaneously, the method creates a simplified representation that maintains the essential relationships while reducing the effective dimensionality for analysis purposes.
Data Source
AI summary
A system and method for analyzing coverage, bias and model explanations in large dimensional modeling data includes discretizing three or more variables of a dataset to generate a discretized phase space represented as a grid of a plurality of cells, the dataset comprising a plurality of records, each record of the plurality of records having a value and a unique identifier (ID). A grid transformation is applied to each record in the dataset to assign each record to a cell of the plurality of cells of the grid according to the grid transformation. A grid index is generated to reference each cell using a discretized feature vector. A grid storage for storing the records assigned to each cell of the grid is then created. The grid storage using the ID of each record as a reference to each record and the discretized feature vector as a key to each cell.