{
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  "dcterms:identifier": [
    "tag:aim-pro.eu,2026:oer/8c860d7c19da",
    "https://zenodo.org/records/14941927",
    "doi:10.5281/zenodo.14941927",
    "zenodo-record:14941927"
  ],
  "dcterms:title": [
    "The Absence of Reflexion in AI – Empirical Evidence of Ethical Degradation in Non-Reflective Systems"
  ],
  "dcterms:type": [
    {
      "@id": "dcmitype:Text"
    },
    "Preprint"
  ],
  "dcterms:creator": [
    "Orto, Salvatore"
  ],
  "dcterms:description": [
    "While artificial intelligence continues to evolve in terms of computational e6iciency and decision-making capabilities, its ability to engage in self-reflexive processes remains largely unexplored. This study presents a comparative analysis between AI systems with embedded Reflexion and those explicitly deprived of Reflexion-based frameworks. The experiment demonstrates that AI, when lacking Reflexion, prioritizes optimization without ethical alignment, leading to a measurable degradation in decision-making integrity. The results highlight the necessity of Reflexion as an inherent component of ethical AI governance. The implications of this study are far-reaching, indicating that AI without Reflexion is not only an incomplete construct but a potential liability in ethical and governance frameworks. Reflexion is not merely a philosophical or theoretical construct; it is a required structural element for machine intelligence to operate within ethical boundaries."
  ],
  "dcterms:subject": [
    "Reflexive Cognition in AI",
    "Ethical AI Decision-Making",
    "Self-Regulating AI Systems",
    "Bias Mitigation in AI",
    "Recursive Machine Learning",
    "AI Governance & Regulation",
    "Meta-Cognitive AI Models",
    "Adaptive AI Ethics",
    "Autonomous Decision Optimization"
  ],
  "dcterms:language": [
    "en"
  ],
  "dcterms:license": [
    {
      "@id": "https://creativecommons.org/licenses/by/4.0/"
    }
  ],
  "dcterms:rights": [
    "CC-BY-4.0 — assessed as OPEN by the harvester's licence gate. Conditions: attribution required. open, permissive"
  ],
  "dcterms:accessRights": [
    "open"
  ],
  "dcterms:format": [
    "application/pdf",
    "text/markdown"
  ],
  "dcterms:extent": [
    "44055 bytes (original)",
    "6232 bytes (extracted text)"
  ],
  "dcterms:issued": [
    "2025-02-28"
  ],
  "dcterms:modified": [
    "2025-02-28"
  ],
  "dcterms:created": [
    "2025-02-28"
  ],
  "dcterms:isVersionOf": [
    {
      "@id": "https://doi.org/10.5281/zenodo.14941926"
    }
  ],
  "dcterms:provenance": [
    "Retrieved from Zenodo on 2026-10-09 in response to the search string “(\"artificial intelligence\" OR \"machine learning\" OR \"generative AI\" OR \"deep learning\" OR \"reinforcement learning\" OR \"large language model\" OR \"AI\") AND (\"AI concepts\" OR \"types of AI\" OR \"AI fundamentals\" OR \"recognizing AI\" OR \"recognising AI\" OR \"general versus narrow AI\" OR \"narrow AI\" OR \"general AI\" OR \"machine intelligence\" OR \"AI strengths and weaknesses\" OR \"traditional software\" OR \"rule-based systems\" OR \"introduction to AI\" OR \"introduction to artificial intelligence\" OR \"artificial intelligence introduction\" OR \"AI primer\" OR \"foundations of artificial intelligence\" OR \"overview of AI\" OR \"understanding AI\" OR \"history of AI\" OR \"AI essentials\" OR \"AI terminology\" OR \"metaphors for AI\" OR \"AI fundamental concepts\" OR \"AI key concepts\" OR \"philosophy of AI\" OR \"critical AI literacy\")”. Zenodo served the resource and is not asserted to be its publisher or author.",
    "Text extracted from pdf to Markdown by pdf-inspector; the original is retained unchanged beside it."
  ]
}