Beyond the Product Lifecycle: A Policy-Driven Systems Intelligence Framework for Governing AI across Organizational Decision Time
Notice bibliographique
Résumé
Artificial intelligence governance has largely been framed around product lifecycles, model deployment stages, and post hoc compliance audits. While these approaches offer necessary safeguards, they are insufficient for governing AI systems that continuously shape organizational decisions across time, scale, and uncertainty. This paper proposes a Policy-Driven Systems Intelligence Framework that reconceptualizes AI governance beyond static lifecycle checkpoints toward dynamic decision-time regulation. The framework integrates systems thinking, institutional policy design, and organizational intelligence to govern how AI influences strategic, operational, and tactical decisions throughout their temporal evolution. The proposed framework introduces decision time as a primary governance dimension, emphasizing anticipation, intervention, and accountability before, during, and after AI-assisted decisions occur. Rather than treating AI as a bounded technical artifact, the model positions AI as an embedded socio-technical actor whose outputs interact with human judgment, organizational incentives, and regulatory norms. Policy instruments such as adaptive guardrails, decision provenance tracking, role-based escalation thresholds, and continuous risk recalibration are embedded directly into decision workflows. At the organizational level, the framework enables alignment between AI behavior and institutional objectives, ethical commitments, and public interest obligations. It supports cross-functional governance by linking executive oversight, operational controls, and frontline decision rights within a unified intelligence architecture. At the policy level, the framework offers regulators and standard-setting bodies a scalable approach for supervising AI systems without stifling innovation, shifting emphasis from model-level compliance to outcome-sensitive decision governance. By foregrounding decision time, the framework addresses emerging risks such as automation bias, policy drift, silent capability expansion, and cumulative harm that often escape lifecycle-based controls. The contribution of this paper is twofold: it advances AI governance theory by introducing decision-centric systems intelligence, and it provides a practical blueprint for organizations and policymakers seeking resilient, transparent, and adaptive governance mechanisms for AI-enabled decision environments. The framework is intended to support trustworthy AI deployment in complex organizational and societal systems where decisions, not products, are the primary locus of impact. Ultimately, it positions governance as an ongoing cognitive and institutional capability that evolves with organizational learning, policy feedback, and societal expectations in rapidly transforming AI-mediated decision ecosystems across sectors and jurisdictions globally.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,035 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,009 | 0,013 |
| Communication savante | 0,026 | 0,003 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».