Spherical AI Model Visualization for Bias and Error Detection
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Solution Overview
Problem
Current AI model explainability methods lack the ability to transparently describe decision-making processes, leading to challenges in identifying and addressing bias, false positives, and false negatives in data sets, which can cause unintended harm and instability in AI systems.
Innovation Solution
A computer-implemented method that normalizes AI model input data, encodes data points into spherical coordinates, generates basis vectors, and renders a spherical model to visualize data distribution, bias, and gradients, enabling intuitive understanding and management of AI model operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional AI model explainability methods are used, then the system complexity is low, but the ability to detect bias and false positives/negatives is insufficient
Solution Approach 1:
The patent transforms data visualization from traditional two-dimensional plots to three-dimensional spherical coordinate system. Data points are mapped using spherical coordinates (ρ, θ, φ) where ρ represents feature magnitude, θ represents feature direction, and φ represents data point distribution. This dimensional transformation enables comprehensive visualization of data distribution, bias detection, and feature relationships that cannot be achieved in lower dimensions, directly improving measurement precision for bias detection while accepting increased system complexity.
2Loss of information
If comprehensive data analysis is performed to improve AI model explainability, then the ability to identify data quality issues is enhanced, but the processing time and computational resources increase
Solution Approach 1:
The patent implements incremental updating of the spherical visualization model. When new data points are added to the AI model training set, only the affected portions of the spherical model are updated rather than recalculating the entire model. This partial action approach maintains comprehensive data quality assessment capabilities while significantly reducing processing time compared to full model recalculation, directly addressing the trade-off between information completeness and processing time.
3Ease of operation
If spherical coordinate system is used for data visualization, then the intuitive understanding of data distribution is improved, but the computational complexity of coordinate transformation increases
Solution Approach 1:
The patent pre-computes and stores the transformation matrices and basis vectors required for spherical coordinate transformations. During data visualization, these pre-computed elements are reused rather than recalculated, significantly reducing the computational complexity of coordinate transformations. This preliminary action maintains the intuitive three-dimensional visualization benefits while minimizing the computational overhead of coordinate system transformations.
Data Source
AI summary
In a first aspect of the invention, there is a computer-implemented method including: normalizing, by the processor set, an artificial intelligence model input data set; encoding, by the processor set, a data point of the data set; converting, by the processor set, the encoded data point into an angle on a spherical coordinate system to determine a basis vector direction for the data within the data set; generating, by the processor set, a basis vector including the encoded data point based on the angle and the normalizing; and generating, by the processor set, instructions to render a spherical model including the basis vector, wherein the instructions are configured to cause a client device to render the spherical model.