From Paradox to Infrastructure: Sustrato.ai and the Encoding of Epistemic Humility

Abstract

This paper documents the methodological contradiction between researching the autonomy of older adults and using generic language models that inherit clinical surveillance biases pre-existing in the academic corpus. To address this friction, we present sustrato.ai, an Al-assisted systematic review platform that encodes interpretive traceability through a structured three-iteration dialogue between a human researcher and Al, backed by an immutable append-only registry with SHA-256 cryptographic verification. The platform was deployed in an empirical pilot that processed 257 scientific articles on artificial intelligence and older adults, identifying a primary subcorpus of 192 documents. Structured extraction reveals that 62.5% of these articles restrict their evaluation to clinical or technical performance metrics, while only 9 studies address leisure granting protagonist agency to the older adult. Simultaneously, the project demonstrates that the barrier to conducting this level of scrutiny is not budgetary: against a literature where 77.6% of funding is endogenous academic patronage, the total automated API processing cost was less than one US dollar. The convergence of these empirical and architectural results allows the following interpretation: the infrastructure used to review the literature conditions what becomes visible within it. Sustrato.ai proposes a reproducible model for methodologically documenting the friction between human judgment and algorithmic classification.

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