A Mathematical Framework for Legacy, Governance, and Decision Integrity in Enterprise AI
cs.CY · cs.AI
Quick verdict
Proposes governance/legacy scoring for enterprise AI with sound math and a matched-coverage synthetic demo; internally consistent, not field-validated.
Summary
The paper presents a design-science framework for measuring 'institutional legacy' — the capacity of an enterprise AI decision system to keep producing lawful, explainable, beneficial outcomes after its designers depart. It contributes (i) a Legacy Score: a penalized geometric mean of six normalized capabilities (knowledge retention, governance, human oversight, adaptability, feedback learning, jurisdictional fidelity) times (1−P) for unresolved risk, with proven boundedness and non-compensation; (ii) Decision Confidence and Risk models (R = I(1−C)) separating evidence from consequence; (iii) authority-aware retrieval with calibrated abstention; (iv) a governed Decision Memory record; (v) Regulatory Change Velocity mapping change exposure to review intervals; and (vi) a federated regulatory knowledge graph. Eight Decision Integrity Rules and a five-hypothesis evaluation protocol are proposed. A fixed-seed demonstration (200 Monte Carlo replications × 10,000 synthetic decisions) shows the geometric score collapses to zero when human oversight is zero while an arithmetic comparator stays at 0.675, and that governance-aware routing at matched 44.47% coverage cuts automated error rate from 12.29% to 8.78%, high-impact errors by ~79%, and expected harm by ~38.5% versus a confidence-only baseline. The central limitation: the data-generating process is stipulated, so results verify internal logic, not real-world value; the dimensions lack operationalized measurement, and no field validation exists.
Review complete
Assessment at a glance
4
risk flags identified
● high 0
● medium 2
● low 2
5 strengths · 5 weaknesses · 5 author questions
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Key claims under review
- Institutional legacy can be formalized as a bounded, non-compensating composite L = [K·G·H·A·F·J]^(1/6)·(1−P) in which any zero essential dimension forces the score to zero (Props 1-2, §13.1 stress test).
- Routing that conditions on consequence and source authority (Eq. 24) reduces automated errors and harm versus confidence-only routing at identical automation coverage (Table 2: 12.29%→8.78% errors; harm 27.36→16.81 per 1,000; authority-deficient automations 206.87→0 per 1,000).
- Regulatory Change Velocity Vr = λrMrDrUr should map to review intervals Tr = k/(Vr+ε), with proven monotonicity producing differentiated schedules (Table 3: 572.5 to 38.4 days for k=30, ε=0.05).
- Controlling legal authority must act as a hard feasibility constraint on Bayesian updating (Eq. 18: posterior zero outside the feasible set) rather than as one weighted observation among others.
- The framework is explicitly a design artifact and research program: the simulation verifies internal behavior only, and all parameters require context-specific calibration and field validation (§13.4, §14, §15).
Strengths
Unusually honest epistemic framing
The abstract, §13 opening, §13.4 'Interpretive boundary', §14 and §15 all explicitly bound the contribution: the simulation is 'an auditable test of internal behavior, not evidence of production performance', and §13.4 names the stipulated error process and absence of human-review modeling.
Matched-coverage baseline is a genuinely careful control
§13.2 re-tunes the confidence-only threshold per replication to match automation coverage; Table 2 confirms both arms at 44.47% [43.55, 45.38], pre-empting the trivial 'fewer automations → fewer errors' artifact.
Strong reproducibility package
Analytical properties proven and numerically verifiable by hand
Concrete empirical pathway
Weaknesses
Monte Carlo results are close to tautological
Core constructs are unoperationalized
No external baselines or comparative positioning
Normative equations without estimation procedures
Vendor illustration sections are loosely coupled to the math
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Scope & Claims
A single-author design-science paper (cs.CY/cs.AI) by an independent researcher proposing a formal vocabulary for 'institutional legacy': the durable capacity of an enterprise AI decision system to keep producing beneficial, lawful, explainable outcomes after its original designers depart. Seven claimed contributions (§1.1): (1) a Legacy Score L = [K·G·H·A·F·J]^(1/6)·(1−P) over six normalized capabilities with a proportional unresolved-risk penalty; (2) Decision Confidence/Risk separation via C = σ(βᵀx) and R = I(1−C) (Eq. 8-9); (3) authority-aware retrieval with hard filters and calibrated abstention (Eq. 13-15); (4) a versioned Decision Memory tuple (Eq. 16) with Bayesian updating zeroed outside legally feasible hypotheses (Eq. 18); (5) Regulatory Change Velocity Vr = λrMrDrUr mapped to review intervals Tr = k/(Vr+ε); (6) a federated regulatory knowledge-graph schema (§10); (7) eight Decision Integrity Rules (§12) and a five-hypothesis evaluation protocol (§14). The abstract states the contribution is 'conceptual and formal rather than field-validated', and the paper holds that line throughout.
The remaining sections cover Methodology Assessment, Statistical Evidence, Results & Reproducibility, Limitations & Risks, and Verdict & Recommendations.
- All 5 strengths & 5 weaknesses, analyzed in detail
- 4 risk flags with severity, section & reviewer questions
- Methods & statistical evidence assessment
- 5 questions a peer reviewer would ask
- Full six-dimension evaluation report & recommendations
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Frequently asked questions
What is the 'Legacy Score' in this paper?
It is a composite index L = [K·G·H·A·F·J]^(1/6) × (1−P) that combines six normalized capabilities — knowledge retention, governance, human oversight, adaptability, feedback learning, and jurisdictional fidelity — and multiplies by a discount for unresolved risk. It is bounded between 0 and 1 and drops to zero if any essential dimension is zero, a deliberate 'non-compensation' property meaning strong dimensions cannot hide a collapsed control.
Is the paper's reported error reduction based on real data?
No. All numbers come from a synthetic Monte Carlo simulation (200 replications of 10,000 decisions each, fixed seed) whose data-generating process is stipulated by the author. The paper states explicitly that the simulation tests internal logic only and is not evidence of production performance or field effectiveness.
Is code available for this paper?
Yes. The arXiv source bundle includes simulation.py with fixed seed 20260722 plus CSV output files for the tables and figures; it requires only Python 3 with NumPy, pandas, and Matplotlib, and rerunning it regenerates the reported numbers deterministically.
Has this framework been validated in any real organization?
No. The paper proposes a field-evaluation protocol with five hypotheses and designs such as shadow-mode deployment and stepped-wedge rollout, but reports no field validation. It positions itself as a design artifact and research program whose dimensions and thresholds must be calibrated per institution.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- Figures appear only as caption text in the extraction (no rendered plots); all plotted values cited in this review were taken from captions, tables, or body text, which contain the full underlying numbers.
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