What question did this study set out to answer?

To propose a governance-oriented architecture that integrates large language models into organizational decision systems while ensuring accountability and compliance.

March 13, 2026AI4 citationsOpen Access

Governance-Oriented Architecture for Explainable Organizational Decision Systems

LLM-Augmented Algorithmic Management: A Governance-Oriented Architecture for Explainable Organizational Decision Systems

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Authors

NHNikolay Hinov MIMaria Ivanova

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Overview

Algorithmic management systems utilize LLMs to enhance decision transparency in organizations, suggesting a new governance framework.

Key Points

To propose a governance-oriented architecture that integrates large language models into organizational decision systems while ensuring accountability and compliance.
Developed a conceptual architecture combining an algorithmic decision core, an LLM-based cognitive interface, and a governance layer.
Illustrated the framework using cross-domain scenarios and a synthetic-trace simulation.
Evaluated the framework through 120 decision events to measure latency and explanation validity.
Mean baseline latency was 100.3 ms, while LLM-augmented latency increased to 115.8 ms (about 15.5% increase).
Achieved a mean explanation validity proxy of 85.6%.
Simulated constraint-satisfaction rate was 94.2% with 113 of 120 events successfully meeting constraints.

Cite This Study

Hinov et al. (2026) studied this question.

synapsesocial.com/papers/69b3aba202a1e69014ccc9e2 https://doi.org/10.3390/ai7030102

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March 13, 2026AI4 citationsOpen Access

Governance-Oriented Architecture for Explainable Organizational Decision Systems

LLM-Augmented Algorithmic Management: A Governance-Oriented Architecture for Explainable Organizational Decision Systems

View Full Paper

Ask AI

Bookmark

Authors

NHNikolay Hinov MIMaria Ivanova

Discussion

Member takes

Overview

Algorithmic management systems utilize LLMs to enhance decision transparency in organizations, suggesting a new governance framework.

Key Points

To propose a governance-oriented architecture that integrates large language models into organizational decision systems while ensuring accountability and compliance.
Developed a conceptual architecture combining an algorithmic decision core, an LLM-based cognitive interface, and a governance layer.
Illustrated the framework using cross-domain scenarios and a synthetic-trace simulation.
Evaluated the framework through 120 decision events to measure latency and explanation validity.
Mean baseline latency was 100.3 ms, while LLM-augmented latency increased to 115.8 ms (about 15.5% increase).
Achieved a mean explanation validity proxy of 85.6%.
Simulated constraint-satisfaction rate was 94.2% with 113 of 120 events successfully meeting constraints.

Cite This Study

Hinov et al. (2026) studied this question.

synapsesocial.com/papers/69b3aba202a1e69014ccc9e2 https://doi.org/10.3390/ai7030102

View Full Paper

Ask AI

Bookmark