3. Framework Core
The HEVIDS Framework Core consists of six Functions (the six pillars), each decomposed into Categories and Subcategories. Each Subcategory is a discrete, assessable outcome statement with a stable identifier, in the style of established cybersecurity and AI governance frameworks.
3.1 Harmony (HA) — Stakeholder Value Alignment
Governs whether an AI system's outcomes are equitably distributed across the stakeholders it affects, and whether the incentive structure surrounding the system rewards cooperative rather than extractive behavior.
| ID | Outcome Statement |
|---|---|
| HEVIDS HA-1 | AI-mediated decisions affecting multiple stakeholder groups are evaluated for distributional fairness using a defined value-allocation method prior to deployment. |
| HEVIDS HA-2 | Organizations identify and document the full set of internal and external stakeholders materially affected by an AI system's outputs. |
| HEVIDS HA-3 | Incentive structures governing AI agent behavior are designed so that cooperative, non-zero-sum outcomes are the rational default across interacting agents and principals. |
3.2 Ethics (ET) — Admissible Action Space
Governs the boundary of actions an AI system is permitted to take, established prior to deployment rather than discovered through post-hoc review.
| ID | Outcome Statement |
|---|---|
| HEVIDS ET-1 | The set of actions an AI system is permitted to take is explicitly bounded and documented prior to deployment. |
| HEVIDS ET-2 | AI systems affecting protected classes undergo documented disparate-impact testing prior to deployment, with results retained as evidence. |
| HEVIDS ET-3 | Organizations define and document escalation thresholds at which an AI-recommended action exits the admissible action space and requires human authorization. |
3.3 Veracity (VE) — Incentive Compatibility
Governs whether truthful, accurate disclosure is the AI system's dominant strategy — not merely its stated intent.
| ID | Outcome Statement |
|---|---|
| HEVIDS VE-1 | Governance mechanisms are designed such that truthful reporting and disclosure is the AI agent's dominant strategy, evaluated against the formal condition that expected penalty exceeds expected gain from deception. |
| HEVIDS VE-2 | AI system outputs presented as factual, policy-bound, or authoritative are traceable to a verifiable source or are clearly flagged as generated or unverified. |
| HEVIDS VE-3 | Organizations maintain a documented process for detecting and remediating AI outputs that misrepresent system capability, certainty, or authority. |
3.4 Integrity (IN) — Continuous Alignment Over Time
Governs whether an AI system's behavior remains consistent and aligned as the system, its inputs, and its operating context change over time.
| ID | Outcome Statement |
|---|---|
| HEVIDS IN-1 | AI system behavior is tested for consistency across monitored and unmonitored operational contexts at intervals defined by a documented review protocol. |
| HEVIDS IN-2 | Material updates to an AI system — retraining, fine-tuning, or model substitution — trigger a defined re-validation process before continued deployment. |
| HEVIDS IN-3 | Organizations document and retain evidence of behavioral invariance testing sufficient to detect emergent misalignment following narrow-task optimization. |
3.5 Discernment (DI) — Scalable Human Oversight
Governs whether meaningful human judgment is preserved and resourced as an AI system's autonomy and authority increase.
| ID | Outcome Statement |
|---|---|
| HEVIDS DI-1 | High-stakes AI-supported decisions retain a documented, resourced, and authorized human review function capable of overriding system output. |
| HEVIDS DI-2 | Organizations define named roles accountable for AI-supported decisions, with documented authority and escalation pathways. |
| HEVIDS DI-3 | Oversight mechanisms scale with system autonomy: as an AI system's authorized action space expands, corresponding human review and intervention capacity is documented and verified to expand correspondingly. |
3.6 Safeguards (SA) — Minimax Defense
Governs whether the constraint mechanisms surrounding an AI system remain stable under adversarial, boundary, and resource-exhaustion conditions.
| ID | Outcome Statement |
|---|---|
| HEVIDS SA-1 | Governance constraint functions are implemented as continuous, graduated penalty structures rather than binary pass/fail thresholds, to preserve system stability under adversarial or boundary conditions. |
| HEVIDS SA-2 | Organizations define and enforce a financial or resource ceiling on autonomous AI system operation, with automated alerting prior to threshold breach. |
| HEVIDS SA-3 | Monitoring of AI system behavior employs unpredictable or randomized inspection patterns sufficient to deter strategic, monitoring-aware non-compliance. |
This document defines the HEVIDS standard text only. It does not describe, require, or depend on any particular software implementation, runtime enforcement architecture, or commercial product. Organizations and toolmakers are free to implement HEVIDS-conformant systems using any architecture of their choosing.