A new framework grades levels of inference capability in data-driven systems to assess compliance with the EU AI Act definition of AI, illustrated via credit scoring workflows.
Machine Learning 1(1):81–106
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Supervised learning across AI systems vindicates a uniform error-driven associationism for cognition, though operating inside advanced computational structures beyond classical associationist models.
An experimental study on 45 ordinal datasets finds Ordinal Gini the strongest splitting criterion among those tested, lowering MAE by over 3.02% versus nominal Gini.
A synthesis paper offering a practical decision guide, flowchart, and table for choosing among seven established information-theoretic measures in AI and agent applications.
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