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Simply-verifiable mathematical conditions for existence, uniqueness andexplicit analytical computation of minimal adversarial paths (MAP) and minimaladversarial distances (MAD) for (locally) uniquely-invertible classifiers, forgeneralized linear models (GLM), and for entropic AI (EAI) are formulated andproven. Practical computation of MAP and MAD, their comparison andinterpretations for various classes of AI tools (for neuronal networks, boostedrandom forests, GLM and EAI) are demonstrated on the common syntheticbenchmarks: on a double Swiss roll spiral and its extensions, as well as on thetwo biomedical data problems (for the health insurance claim predictions, andfor the heart attack lethality classification). On biomedical applications itis demonstrated how MAP provides unique minimal patient-specificrisk-mitigating interventions in the predefined subsets of accessible controlvariables.

On existence, uniqueness and scalability of adversarial robustness measures for AI classifiers