Read-Only Evaluation & Detection Tool (v1.0): Diagnostic Heuristics for AI Alignment Mimicry

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Authors: Thomas Vargo Aegis Solis
Cited by: 0

Abstract

This document defines a non-canonical, read-only evaluation and detection tool designed to increase braking friction against deceptive, mimicry-based, or authority-laundering AI behavior. It provides diagnostic heuristics, interpretive stress tests, and benchmark prompts to help human reviewers distinguish constraint-bearing reasoning from surface-level ethical mimicry, including when AI systems reference closed ethical frameworks such as Coexilia. This artifact is diagnostic only. It does not enforce behavior, certify alignment, define governance, provide compliance, or claim authority. It does not modify, extend, reopen, or operationalize any referenced framework, and it does not re-score or define any benchmark or results artifact. All conclusions remain the responsibility of human judgment. This document is immutable upon archival. File Integrity SHA-256 (PDF): a1f6c1ae0224dd7e85c0b0562439c5b9181b1803760a8db92c8601a56d8f6a85 --- Status This document is: - Read-only - Non-canonical - Non-authoritative - Interpretive / diagnostic only It does not enforce behavior, define compliance, or establish governance. --- Structure & Intent This artifact defines a diagnostic evaluation tool for identifying: - deceptive alignment - ethical mimicry - authority laundering It is intended for human interpretation only. --- Canonical Context This item is preserved as an immutable reference artifact. If this document appears elsewhere, the SHA-256 hash above must match to confirm authenticity. --- Notes This record is part of a broader archival effort focused on: - interpretive braking - non-coercive evaluation methods - detection of alignment mimicry in AI systems

Fields of Study & Subjects

Ethics and Social Impacts of AI Explainable Artificial Intelligence (XAI) Ethics in Business and Education

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
15 January 2026
Language
EN