arXiv:2606.16411v1 Announce Type: new Abstract: The Jensen-Shannon divergence is widely reported as a scalar measure of fidelity for synthetic tabular data. Yet, in practice, it is estimated from finite samples using protocols that are often underspecified. This creates a measurement problem. Although the population divergence is well defined, the empirical value depends on the estimator family, sampling protocol, calibration, dimensionality, and class balance. We show that different protocols can yield non-comparable values: marginal-based estimators ignore dependencies in the joint distribut
This paper highlights a critical issue in the practical application of AI evaluation metrics, emerging as the field increasingly relies on robust and reliable assessments for model fidelity and deployment.
A strategic reader should care because flawed or inconsistent evaluation metrics can lead to incorrect conclusions about AI model performance, impacting investment, R&D, and deployment decisions, especially in critical applications.
The understanding of Jensen-Shannon Divergence as an evaluation metric now includes a stronger caution regarding estimator choice and sampling protocols, demanding greater rigor in AI research and development.
- · Researchers focused on AI evaluation rigor
- · AI ethicists
- · Developers of robust AI evaluation tools
- · AI models evaluated using underspecified protocols
- · Organizations relying solely on headline JSD scores
- · Uncritical AI research
Increased scrutiny and demand for transparency in AI model evaluation methodology.
Development of standardized protocols and best practices for applying and interpreting divergence metrics in AI.
A potential slowdown in AI deployment if evaluation uncertainties create significant regulatory or trust hurdles.
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