Industry ApplicationsFebruary 12, 202638 min read

Fairness Score Design for Insurance AI: Discrimination Detection Through Correlation Matrix Analysis

Evaluating algorithmic discrimination in insurance pricing and underwriting using correlation matrices and responsibility-gated fairness enforcement

Insurance AI systems can inherit historical bias from training data. Detecting discrimination requires more than demographic-parity checks, including analysis of indirect pathways between protected attributes and pricing features. This paper introduces a correlation-matrix-based fairness score to detect direct and proxy discrimination, paired with gate-based enforcement before decisions reach customers.

insurancefairnessdiscrimination-detectioncorrelation-matrixbiasethicsgovernance
MathematicsJanuary 20, 202624 min read

Linear Algebra Model for Negative Correlation Detection Across Business Universes

Using eigendecomposition of correlation matrices to identify conflicting objectives across business universes

When business universes optimize in opposing directions, organizations incur both direct conflict cost and wasted optimization effort. This paper develops a linear-algebra framework for detecting negative correlations using correlation matrices, eigendecomposition, and spectral analysis. Negative eigenvalues in inter-universe correlation structures identify conflict clusters that require governance intervention rather than additional local optimization.

linear-algebracorrelation-matrixeigendecompositionconflict-detectionmulti-universespectral-analysis