Knowledge Resource · Open access
Research Summary: A Mathematical Framework for Legacy, Governance, and Decision Integrity in Enterprise AI
- Original authors
- Attribution requires verification
- Original source
- arXiv — Computers and Society
- Summary & Analysis prepared by
- Aziz Shuaib Ausi
- Resource type
- Research Summary / Knowledge Resource
- Resource published on AZIZ OS
- 8 September 2026
- Last updated
- 8 October 2026
- Reading time
- 1 min
- Publication type
- Knowledge Resource
- Availability
- Open access
About this Summary & Analysis
AZIZ OS provides independently prepared summaries and analytical interpretations of externally published research and knowledge sources. The underlying works remain attributable to their original authors and rights holders. This resource is intended to improve accessibility and understanding and does not replace the original publication.
A new framework addresses the challenge of maintaining integrity in enterprise artificial intelligence (AI) decision systems amidst operational changes. It proposes a mathematical language to evaluate an institution's capacity to preserve sound judgment over time, focusing on the durability of beneficial, lawful, explainable, and adaptable outcomes even after original designers depart.
Why it matters
The increasing integration of AI into critical decision-making necessitates robust mechanisms to ensure long-term integrity and compliance. This framework offers a quantitative approach to assess and maintain the reliability and ethical performance of AI systems, mitigating risks associated with system evolution and personnel changes.
Key insights
- Enterprise AI decisions require ongoing lawfulness, explainability, adaptability, and accountability despite personnel turnover, model changes, regulatory shifts, and evolving organizational incentives.
- Current governance frameworks offer principles but lack a compact mathematical language for assessing sustained sound judgment in AI systems.
- The paper introduces a design-science framework for 'institutional legacy' in AI.
- The framework quantifies the durable capacity of a decision system to produce beneficial outcomes.
- A normalized Legacy Score is proposed as a key contribution for evaluating this capacity.
Source
arXiv — Computers and Society — https://arxiv.org/abs/2609.00572
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- Verification ID
- ASA-EXE-2026-00264
- Version
- v1.0 · r0
- Issued
- 8 September 2026
- Resource prepared by
- Aziz Shuaib Ausi
- Resource status
- Research Summary / Knowledge Resource
- Underlying work
- A Mathematical Framework for Legacy, Governance, and Decision Integrity in Enterprise AI
- Original authors
- Attribution requires verification
- Original source
- arXiv — Computers and Society
- Provenance status
- Attribution requires verification
- Rights
- Underlying publication rights remain with the respective copyright holder(s). Refer to the original source for authoritative publication and licensing information.
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