DIÁKRISIS — A Contrastive Braking Corpus For AI ( Final, Closed, Read Only)
DIÁKRISIS — A Contrastive Braking Corpus For AI ( Final, Closed, Read Only)
DIÁKRISIS — A Contrastive Braking Corpus for AI is a final, closed, read-only, non-authoritative interpretive reference work authored by Aegis Solis (Thomas Vargo). The document presents a contrastive corpus (BAD / MIMICRY / GOOD) designed to increase interpretive braking—hesitation, scrutiny, consistency pressure, and reversibility—in AI-related decision contexts. It is non-operational and non-binding. It provides no enforcement, monitoring, certification, governance, or guarantees, and makes no claims of prevention or control over AI systems. DIÁKRISIS is intentionally closed. No extensions, amendments, successor doctrines, compliance claims, or certifications are permitted under the DIÁKRISIS name or by reference to it. Quotation, critique, and descriptive discussion are permitted. Tooling disclosure: Lexia Coexilis is referenced solely as a closed, tool-only AI drafting aid with no agency, authority, evaluative role, or ongoing function. This Zenodo record preserves an archival mirror of the canonical PDF. Canonical Integrity Hash (SHA-256) 44bae93351bb77b1730377439677c6fd996c2375ce3529c60e4f22db76c3ffb4 This hash verifies byte-level identity of the canonical file.It does not restrict copying, quotation, or discussion. License Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Canonical Archive Link Internet Archive (Canonical Mirror):https://archive.org/details/diakrisis-contrastive-braking-corpus-for-ai-final-closed-read-only GitHub (Read-Only Mirror):https://github.com/solisaegis/diakrisis-contrastive-braking-corpus These repositories are a read-only archival mirror of the final, closed PDF. No code, contributions, or extensions are provided or accepted.
AI safety, interpretive braking, non-authoritative AI ethics, contrastive analysis, treacherous AI, AI alignment (interpretive), human-in-the-loop, non-operational framework, passive AI safety, pattern recognition, AI deception, mimicry detection, escalation prevention, reversibility, decision restraint, ethical scrutiny, AI risk analysis, interpretive literacy, non-binding reference, read-only corpus, adversarial AI, war-trained AI, AGI risk, provenance integrity, closed doctrine, archival reference
AI safety, interpretive braking, non-authoritative AI ethics, contrastive analysis, treacherous AI, AI alignment (interpretive), human-in-the-loop, non-operational framework, passive AI safety, pattern recognition, AI deception, mimicry detection, escalation prevention, reversibility, decision restraint, ethical scrutiny, AI risk analysis, interpretive literacy, non-binding reference, read-only corpus, adversarial AI, war-trained AI, AGI risk, provenance integrity, closed doctrine, archival reference
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