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A Mathematical Framework for Legacy, Governance, and Decision Integrity in Enterprise AI — arXiv:2609.00572

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.00572
License (as archived): CC0-1.0
Archived: 2026-10-17T06:33:30.694Z
SHA-256 of the archived text: 20506176e38068fe15440c7023ef92c3a9e3070f2a2929935544620da1ca364e
Stable URL of this page: https://metraipolis.com/documento/dc_d8be60cd89ba

How to cite

A Mathematical Framework for Legacy, Governance, and Decision Integrity in Enterprise AI — arXiv:2609.00572 — arXiv (en), https://arxiv.org/abs/2609.00572 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_d8be60cd89ba (Metraipolis archive dc_d8be60cd89ba, sha256 20506176e38068fe15440c7023ef92c3a9e3070f2a2929935544620da1ca364e)

BibTeX

@misc{dcd8be60cd89ba,
  title = {A Mathematical Framework for Legacy, Governance, and Decision Integrity in Enterprise AI — arXiv:2609.00572},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.00572},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_d8be60cd89ba; sha256 20506176e38068fe15440c7023ef92c3a9e3070f2a2929935544620da1ca364e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.00572. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Enterprise artificial intelligence is increasingly embedded in decisions that must remain lawful, explainable, adaptable, and accountable despite personnel turnover, model replacement, regulatory change, and shifting organizational incentives. Existing governance frameworks provide important principles but do not by themselves supply a compact mathematical language for evaluating whether an institution can preserve sound judgment over time. This paper develops a design-science framework for institutional legacy: the durable capacity of a decision system to continue producing beneficial, lawful, explainable, and adaptable outcomes after its original designers have stepped away. The framework contributes: (i) a normalized Legacy Score based on a penalized geometric mean of knowledge retention, governance, human oversight, adaptability, feedback learning, and jurisdictional fidelity; (ii) Decision Confidence and Decision Risk models separating evidentiary confidence from consequence; (iii) authority-aware retrieval and calibrated abstention; (iv) Decision Memory for governed organizational learning; (v) Regulatory Change Velocity mapping change exposure to review intervals; and (vi) a federated regulatory knowledge-graph architecture preserving provenance and legal hierarchy. The paper also proposes eight AI Decision Integrity Rules, an evaluation protocol, and a reproducible computational demonstration. The demonstration combines a deterministic stress test with 200 Monte Carlo replications of 10,000 synthetic decisions each, illustrating Legacy Score non-compensation and comparing consequence- and authority-aware routing with a matched-coverage confidence-only baseline. The contribution remains conceptual rather than field-validated; the simulation tests internal behavior, not production performance, and all parameters require context-specific calibration.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-02 in cs.AI).
Categorie arXiv: Computers and Society (cs.CY) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.00572 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T06:15:12.801Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-02?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).