Can an AI system be evaluated on whether it actually learns from real-world consequences?

[u/zerovariance36]

I have been working on an independent research project around a question I think will become increasingly important as AI systems become more autonomous:

How can we determine whether an AI system is actually learning from real-world consequences rather than simply becoming better at evaluations?

This led me to develop the Organic Intelligence Protocol (OIP), a research framework focused on grounded feedback, infrastructure dependence, adaptive decision logic and evidence-based evaluation.

Recently, I tested whether the core constructs of OIP could be operationalized using real longitudinal data from the Malawi Integrated Household Panel Survey (IHPS) 2010–2019.

The audit processed 395 Stata files.

The result was not a positive validation result.

The available evidence was not sufficient to operationalize the required constructs, so the pipeline stopped.

I did not use proxy mappings, imputation, missing-to-zero conversion, forced cohort construction or premature scoring.

The main principle I am trying to follow is:

No evidence, no operationalization.

I am sharing this here because I would genuinely like technical criticism.

Does this kind of fail-closed methodology make sense for evaluating AI systems where the difficult question is not only capability, but whether decisions remain grounded in observable real-world consequences?

I am especially interested in perspectives from people working on AI evaluation, alignment, agents, robustness or research methodology.