AICOS: An Evidence-Based Institutional Decision Reliability Infrastructure for Replayable, Auditable and Calibrated Organizational Decisions
Description
This paper introduces AICOS (AI-enabled Institutional Decision Reliability Infrastructure), a governance-oriented framework designed to improve the reliability, transparency and reproducibility of institutional decision processes.
The proposed architecture connects evidence management, policy alignment, intelligence processing, decision execution, replay mechanisms, outcome evaluation and calibration within a structured lifecycle.
Unlike approaches focused only on prediction performance, AICOS emphasizes the complete decision lifecycle by preserving relationships between evidence, decisions and measurable outcomes.
The framework introduces concepts including replayable decisions, evidence-based governance, decision reliability measurement and outcome calibration.
This research presents an architectural and conceptual framework. Further empirical validation across different institutional environments is required to evaluate practical performance and scalability.
Other (English)
This research explores the concept of Institutional Decision Reliability Infrastructure as an emerging approach for trustworthy artificial intelligence adoption.
The study focuses on the relationship between evidence quality, governance mechanisms, decision traceability, replay capability and outcome calibration.
AICOS is designed as a framework for organizations that require accountable and reviewable decision processes in complex operational environments.
The proposed architecture emphasizes:
- evidence-based decision formation,
- reproducible decision analysis,
- audit-oriented governance,
- continuous learning through outcome feedback.
The research distinguishes between artificial intelligence model performance and institutional decision reliability, proposing that reliable decision systems require both analytical capability and governance infrastructure.
Future work includes empirical validation, domain-specific implementations and comparative evaluation across different institutional environments.
Files
AICOS_Paper_001_v1.0.pdf
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Additional details
Software
- Repository URL
- https://orcid.org/0009-0009-5063-999X
- Programming language
- Python
- Development Status
- Active
References
- 1. National Institute of Standards and Technology (NIST). Artificial Intelligence Risk Management Framework (AI RMF 1.0). NIST AI 100-1, 2023. https://doi.org/10.6028/NIST.AI.100-1 2. European Commission High-Level Expert Group on Artificial Intelligence. Ethics Guidelines for Trustworthy AI. European Commission, 2019. 3. Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., Pedreschi, D. A Survey of Methods for Explaining Black Box Models. ACM Computing Surveys, 51(5), 2018. 4. Ribeiro, M. T., Singh, S., Guestrin, C. Why Should I Trust You? Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016. 5. Sculley, D., et al. Hidden Technical Debt in Machine Learning Systems. Advances in Neural Information Processing Systems, 2015. 6. Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., Gebru, T. Model Cards for Model Reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency, 2019. 7. Doshi-Velez, F., Kim, B. Towards A Rigorous Science of Interpretable Machine Learning. arXiv:1702.08608, 2017. 8. ISO/IEC. ISO/IEC 42001:2023 Artificial Intelligence Management System. International Organization for Standardization, 2023. 9. Russell, S., Norvig, P. Artificial Intelligence: A Modern Approach. Pearson, 2021. 10. Amershi, S., et al. Software Engineering for Machine Learning: A Case Study. IEEE/ACM International Conference on Software Engineering, 2019.