Published March 4, 2026 | Version v1.0 (First public release)
Preprint Open

AICOS®: Decision Authority Infrastructure for Artificial Intelligence Governance

Authors/Creators

  • 1. Independent Researcher Founder & Architect, AICOS® Decision Authority Infrastructure

Description

This paper introduces AICOS® (Artificial Intelligence Control and Oversight System), a governance-first technological architecture designed to manage decision authority in artificial intelligence systems.

While modern artificial intelligence research has focused primarily on model capability, computational performance, and data scale, the governance of AI-assisted decision processes remains an unresolved structural challenge. As AI systems become embedded in critical infrastructures such as finance, healthcare, energy systems, and public governance, the need for accountable decision mechanisms becomes increasingly important.

This research proposes the concept of Decision Authority Infrastructure as a new architectural layer within artificial intelligence systems. The proposed infrastructure operates between AI models and real-world execution environments and ensures that AI-generated outputs are validated through governance constraints before influencing operational decisions.

The AICOS® architecture integrates several governance mechanisms, including uncertainty thresholds, decision risk evaluation, irreversibility analysis, deterministic decision replay, and human-final authority enforcement. These mechanisms enable transparent, auditable, and institutionally aligned decision processes.

Mathematically, the decision pipeline is defined as a structured transformation from data inputs to final actions under governance constraints. The framework models decision authority as a constrained optimization process and introduces formal representations for decision uncertainty, decision risk, and irreversible outcomes.

The paper argues that the next phase of global artificial intelligence deployment will require governance-native infrastructure capable of ensuring safe, accountable, and traceable AI-assisted decisions. Decision Authority Infrastructure represents a foundational step toward integrating institutional authority structures directly into artificial intelligence systems.

The AICOS® framework contributes a new perspective to AI governance research by proposing a technological infrastructure that bridges artificial intelligence capabilities with institutional decision authority.

This work aims to stimulate further research on governance-native AI architectures and decision accountability mechanisms in advanced artificial intelligence systems.

Other (English)

This work proposes a governance-oriented infrastructure for artificial intelligence systems and focuses on the structural challenge of decision authority in AI-assisted environments.

The AICOS® framework is designed as a technological architecture that operates between AI model outputs and real-world execution systems. Its purpose is not to replace artificial intelligence models but to introduce an institutional governance layer capable of validating decisions generated by AI systems.

The proposed architecture integrates formal governance mechanisms such as uncertainty management, decision risk evaluation, irreversibility analysis, deterministic decision replay, and human-final authority principles. These mechanisms allow decisions to be evaluated according to predefined governance policies before operational implementation.

The study contributes a conceptual and mathematical framework describing how decision authority can be represented within artificial intelligence infrastructures. The model defines decision pipelines as transformations from input data to final actions under governance constraints and introduces risk and uncertainty parameters for structured decision escalation.

This work aims to stimulate research on governance-native artificial intelligence architectures and to encourage the development of technological infrastructures capable of supporting responsible AI deployment in critical systems.

The research does not propose autonomous AI decision-making. Instead, it emphasizes the importance of maintaining human authority over high-impact decisions while allowing AI systems to provide analytical support within structured governance environments.

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AICOS_Decision_Authority_Infrastructure_AI_Whitepaper.tex.pdf

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Additional details

Software

Repository URL
https://doi.org/10.5281/zenodo.18460985

References

  • Russell, S. (2019). Human Compatible: Artificial Intelligence and the Problem of Control. Viking. Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. Amodei, D., et al. (2016). Concrete Problems in AI Safety. arXiv:1606.06565. NIST (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology. European Commission (2024). Artificial Intelligence Act. Floridi, L., et al. (2018). AI4People — An Ethical Framework for a Good AI Society. Minds and Machines. Doshi-Velez, F., & Kim, B. (2017). Towards a Rigorous Science of Interpretable Machine Learning. Mitchell, M. (2019). Artificial Intelligence: A Guide for Thinking Humans. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age. Silver, D., et al. (2016). Mastering the Game of Go with Deep Neural Networks and Tree Search. Nature. Kahneman, D. (2011). Thinking, Fast and Slow. Tversky, A., & Kahneman, D. (1974). Judgment Under Uncertainty: Heuristics and Biases. Varshney, K. R. (2019). Trustworthy Machine Learning. OECD (2019). OECD Principles on Artificial Intelligence.