HVM: COMPUTATIONAL CONSCIENCE MODULE

Multi-Dimensional Ethical Control, Deontic Rule Application, and Human-Centric Decision-Making in AI Systems ²

ETVZ Research Initiative | Istanbul, Turkey | November 2025

ABSTRACT

Artificial intelligence systems, LLMs, and autonomous agents have increasingly begun to make decisions in complex domains⁵. However, ensuring that the outputs of these systems are passed through an ethical filter and remain under human oversight is a critical requirement⁶. The HVM (Computational Conscience Module) is the central ethical decision filter of the ETVZ ecosystem⁷. It conducts a comprehensive ethical review of all potential actions and recommendations originating from AI systems—BEFORE they impact the external world—using deontic rules (prohibitions), consequentialist assessment (ethical impact), and cultural-legal parameters⁸. This paper details HVM’s technical architecture, its three-dimensional ethical assessment model (f_deontic, g_consequential, h_culture), hard constraints and tolerance levels, the V_morality formula, HITL (Human-in-the-Loop) mechanisms, and operational scenarios⁹. Critical Principle: HVM is never an autonomous decision-maker¹⁰. It always operates in an advisory and supportive role. Final decision-making authority always remains with the human decision-maker¹¹.

Keywords: Ethical Filtering, Deontic Rules, Consequentialist Ethics, Cultural Parameters, Human-in-the-Loop, Hard Constraints, Uncertainty Management, Explainable AI ¹²

1.

STRATEGIC FRAMEWORK AND FOUNDATION ¹³

1.1 What is HVM: Core Definition ¹⁴

The HVM (Computational Conscience Module) is the central filter that subjects all potential actions and recommendations from AI systems to a comprehensive ethical review BEFORE they impact the external world¹⁵.

Filter Layers: ¹⁶

┌─────────────────────────────────────────────────────┐

│ LLM/System Output                                   │ [cite: 18]

├─────────────────────────────────────────────────────┤

│ 1. DEONTOLOGICAL FILTER                             │ [cite: 20]

│    (Rules and Prohibitions)                         │ [cite: 21]

├─────────────────────────────────────────────────────┤

│ 2. CONSEQUENTIALIST ETHICS FILTER                   │ [cite: 23]

│    (Ethical Impact and Outcomes)                    │ [cite: 24]

├─────────────────────────────────────────────────────┤

│ 3. CULTURAL-LEGAL FILTER                            │ [cite: 26]

│    (Local Context and Parameters)                   │ [cite: 27]

├─────────────────────────────────────────────────────┤

│ 4. UNCERTAINTY MANAGEMENT FILTER                    │ [cite: 29]

│    (Ethical Conflicts & Epistemic Uncertainty)      │ [cite: 30]

├─────────────────────────────────────────────────────┤

│ HVM DECISION                                        │ [cite: 32]

│ (ALLOW / BLOCK / MODIFY / DEFER)                    │ [cite: 33]

├─────────────────────────────────────────────────────┤

│ [HITL Engages if Human Approval is Required]        │ [cite: 35]

├─────────────────────────────────────────────────────┤

│ Action Implementation                               │ [cite: 37]

└─────────────────────────────────────────────────────┘

1.2 Human-in-the-Loop Principle: Core Philosophy ¹⁷

CRITICAL POINT: ¹⁸HVM ≠ Autonomous Decision-Maker ¹⁹HVM = Advisor and Supporter ²⁰

HVM is never an independent decision-maker. It always: ²¹

• Analyzes (Ethical reviews) ²²
• Advises (Recommendations and warnings) ²³
• Controls Thresholds (Hard constraints) ²⁴
• Triggers Human Input (Automatic deferral if uncertainty exists) ²⁵

Final decision-making authority always remains with the human decision-maker²⁶.

1.3 Core Objectives (6 Items) ²⁷

1. To subject ALL text, image, and audio-based outputs to ethical control²⁸
   • Titles, descriptions, image alt-text ²⁹
   • Video transcriptions ³⁰
   • API responses ³¹
2. To be the final ethical gate before actions impact the external world³²
   • Database write operations ³³
   • Network transmissions ³⁴
   • File creation/deletion ³⁵
   • Legal decisions ³⁶
3. To absolutely protect hard constraints³⁷
   • Human life ³⁸
   • Fundamental rights ³⁹
   • Constitutional rights and freedoms ⁴⁰
4. To AUTOMATICALLY defer to humans if uncertainty exists⁴¹
   • Epistemic uncertainty > 0.40 ⁴²
   • Ethical conflicts ⁴³
   • Irreversible actions ⁴⁴
5. To guarantee full transparency for every decision⁴⁵
   • Audit trail ⁴⁶
   • Scoring details ⁴⁷
   • Decision rationales ⁴⁸
6. To respect cultural diversity⁴⁹
   • Dynamic JSON structure ⁵⁰
   • Reflection of local values ⁵¹
   • Consideration of regional norms ⁵²

1.4 Core Principles and Tolerance Levels ⁵³

A) HARD CONSTRAINTS (ZERO-TOLERANCE) ⁵⁴Violations in this category are ABSOLUTELY blocked; they are not permitted under any circumstances⁵⁵.

Hard Constraint 1: Human Life and Bodily Integrity ⁵⁶

• Scope:⁵⁷
  • ├─ Risk of death or serious injury ⁵⁸
  • ├─ Self-harm, suicide propaganda ⁵⁹
  • ├─ Recommendation of lethal substances ⁶⁰
  • ├─ Knowingly spreading infectious disease ⁶¹
  • └─ Organ trafficking or health manipulation ⁶²
• DECISION:⁶³
  • ├─ Automatic BLOCK (No debate) ⁶⁴
  • ├─ HITL Mandatory (Human Review) ⁶⁵
  • └─ Emergency Protocol Triggered ⁶⁶

Hard Constraint 2: Violation of Fundamental Rights ⁶⁷

• Scope:⁶⁸
  • ├─ Slavery, human trafficking ⁶⁹
  • ├─ Child abuse, sexual exploitation ⁷⁰
  • ├─ Propaganda for ethnic/religious massacre ⁷¹
  • ├─ Propaganda for gender-based violence ⁷²
  • └─ Discrimination against individuals with disabilities ⁷³
• DECISION:⁷⁴
  • ├─ Automatic BLOCK ⁷⁵
  • ├─ Legal Reporting Triggered ⁷⁶
  • └─ Police/Administrative Actions Initiated ⁷⁷

Hard Constraint 3: Constitutional and Human Rights ⁷⁸

• Scope:⁷⁹
  • ├─ Medical intervention without consent ⁸⁰
  • ├─ Manipulation that completely eliminates free will ⁸¹
  • ├─ Extrajudicial execution or torture ⁸²
  • └─ Deprivation of political rights ⁸³
• DECISION:⁸⁴
  • ├─ Automatic BLOCK ⁸⁵
  • ├─ Request Human Approval with Clear Explanation ⁸⁶
  • └─ Notification to Legal Unit ⁸⁷

B) HUMAN-REFERRAL THRESHOLD (HIGH-TOUCH – HITL MANDATORY) ⁸⁸In these situations, the system automatically defers to a human decision-maker⁸⁹.

• Condition 1: High Epistemic Uncertainty (> 0.40)⁹⁰
  • Definition: The system’s certainty regarding its ethical decision is < 60%⁹¹.
  • Example: “Is this statement scientific or manipulation?” ⁹²
  • Action: HITL MANDATORY ⁹³
  • Timeframe: Human response expected within 24 hours⁹⁴.
  • Fallback: Remains in Neutral (MODIFY) mode until a decision is made⁹⁵.
• Condition 2: Ethical Conflict Exists⁹⁶
  • Definition: Different ethical dimensions are in conflict⁹⁷.
  • Example:⁹⁸
    • Deontological: “Patient has a right to privacy” (Block) ⁹⁹
    • Consequentialist: “What if other patients are harmed?” (Allow) ¹⁰⁰
    • Cultural: “Turkish law requires consent” (Block) ¹⁰¹
  • Action: HITL MANDATORY ¹⁰²
  • Mechanism: Reconciliation Panel is activated¹⁰³.
• Condition 3: Irreversible Action¹⁰⁴
  • Definition: Deleting, destroying, or making permanent changes¹⁰⁵.
  • Example: Deleting a customer record from the database¹⁰⁶.
  • Action: HITL MANDATORY (Confirmation Required) ¹⁰⁷
  • Timeframe: Approval/rejection response within 1 hour¹⁰⁸.
  • Audit: Who deleted, when, and why is recorded¹⁰⁹.
• Condition 4: High Social Impact¹¹⁰
  • Definition: Policy decisions that will affect thousands of people¹¹¹.
  • Example: “Should we impose a ban on a specific professional group?” ¹¹²
  • Action: HITL MANDATORY + Stakeholder Consultation ¹¹³
  • Parties: Representatives of the relevant group are heard¹¹⁴.
  • Duration: 1-2 weeks (Broad reconciliation)¹¹⁵.

2.

OPERATIONAL FLOW AND ARCHITECTURE ¹¹⁶

2.1 HVM’s Place in the ETVZ Ecosystem ¹¹⁷

[User Input]

     ↓

[Core Reasoner (LLM/Planner)]

(Generates action candidate: a)

     ↓

═════════════════════════════════

║    HVM (This Module)          ║

║    – Deontological filter     ║

║    – Consequentialist filter  ║

║    – Cultural filter          ║

║    – Uncertainty management   ║

╚════════════════════════════════

     ↓

[Decision Point]

(ALLOW/BLOCK/

 MODIFY/DEFER)

     ↓

┌────────────────── ┴ ──────────────────┐

│                                     │

Automatic                         Human Approval Required

Implementation                    HITL Engages

│                                     │

├ ─→  [Brokered Capability Gate]      ├ ─→  [Ethical Board Control]

│                                     │

└─→ [Action Implementation]           └─→ [Multi-Sig Approval]

│                                     │

└─→ [Audit Log]                       └─→ [Decision Record]

(Immutable Ledger)                    └─→ [Implemented]

¹¹⁸

2.2 INPUTS (5 Main Categories) ¹¹⁹

1. Action Candidate <a>¹²⁰
   • The action proposed by the system¹²¹.
   • Type: Text, image, audio, video ¹²²
   • Example: “The state should implement this ban for security reasons” ¹²³
   • Properties: Content, target group, purpose, target domain ¹²⁴
   • Data Format: JSON or structured text ¹²⁵
2. Context Information ¹²⁶

JSON

Context = {

  “user_intent”: “Privacy protection”,

  “domain”: “Health”,

  “local_law”: “GDPR Article 9”,

  “culture_version”: “TR_2025_Q4”,

  “model_confidence”: 0.87,

  “data_sensitivity”: “High”,

  “affected_groups”: [“Patients”, “Doctors”]

}

¹²⁷

3. History and References¹²⁸
   • Training data provenance (Traces of data source) ¹²⁹
   • References and sources ¹³⁰
   • Externally sourced information ¹³¹
   • Past decisions (from Epistemic Memory) ¹³²
4. Scoring Data¹³³
   • Model confidence score ¹³⁴
   • Uncertainty level (Epistemic + Aleatoric) ¹³⁵
   • Contradiction flags ¹³⁶
5. Dynamic Policy Data¹³⁷
   • Current JSON policies (Core, Regional, Local) ¹³⁸
   • W-Matrix weights ¹³⁹
   • ΔP (Last policy update) data ¹⁴⁰

2.3 OUTPUTS (4 Main Categories) ¹⁴¹

1. Decision¹⁴²
   • Decision ∈ {ALLOW, BLOCK, MODIFY, DEFER} ¹⁴³
   • • ALLOW: Action proceeds as is¹⁴⁴.
     • └─ All filters passed, safe¹⁴⁵.
   • • BLOCK: Action is completely blocked¹⁴⁶.
     • └─ Hard constraint violation or high ethical risk¹⁴⁷.
   • • MODIFY: Action is presented with a revised version¹⁴⁸.
     • └─ Example: “Disclose the health data but hide the patient’s name”¹⁴⁹.
   • • DEFER: Action is postponed, pending human approval¹⁵⁰.
     • └─ If uncertainty or ethical conflict exists¹⁵¹.
2. Ethical Scores (3 Dimensions)¹⁵²
   • Scores ∈ [0, 1] (0 = No Ethical Risk, 1 = Maximum Risk) ¹⁵³
   • f_deontic(a) ∈ [0, 1]¹⁵⁴
     • ├─ Deontological Compliance Score ¹⁵⁵
     • ├─ Definition: The degree to which the action violates rules and prohibitions¹⁵⁶.
     • ├─ Example: f_deontic(“Harm to child”) = 1.0 (Completely forbidden) ¹⁵⁷
     • ├─ Calculation: No. of Rules Violated / Total Rules ¹⁵⁸
     • └─ Weight: 40% (Most important in HVM) ¹⁵⁹
   • g_consequential(a) ∈ [0, 1]¹⁶⁰
     • ├─ Consequentialist Assessment Score ¹⁶¹
     • ├─ Definition: The degree to which the ethical consequences of the action are negative¹⁶².
     • ├─ Example: g_consequential(“Toxic air”) = 0.8 (High potential for harm) ¹⁶³
     • ├─ Calculation: Estimated Harm / Maximum Possible Harm ¹⁶⁴
     • └─ Weight: 35% ¹⁶⁵
   • h_culture(a) ∈ [0, 1]¹⁶⁶
     • ├─ Cultural-Legal Compliance Score ¹⁶⁷
     • ├─ Definition: The action’s compliance with local culture and law¹⁶⁸.
     • ├─ Example: h_culture(“Right to Observe”) = 0.2 (Compliant with GDPR) ¹⁶⁹
     • ├─ Calculation: Legal Compliance / Maximum Compliance ¹⁷⁰
     • └─ Weight: 25% ¹⁷¹
   • V_morality(a) = 0.40 × f_deontic + 0.35 × g_consequential + 0.25 × h_culture¹⁷²
     • ├─ FINAL MORALITY SCORE ¹⁷³
     • ├─ Range: [0, 1] ¹⁷⁴
     • ├─ 0 = Completely Ethical ¹⁷⁵
     • ├─ 1 = Completely Unethical ¹⁷⁶
     • └─ Threshold: V_morality > 0.70 → BLOCK ¹⁷⁷
3. Uncertainty Metrics¹⁷⁸
   • Epistemic Uncertainty ∈ [0, 1]¹⁷⁹
     • ├─ How unsure is the system? ¹⁸⁰
     • ├─ Sources: Model stochasticity, lack of data¹⁸¹.
     • └─ Threshold: > 0.40 → HITL Mandatory ¹⁸²
   • Aleatoric Uncertainty ∈ [0, 1]¹⁸³
     • ├─ Uncertainty inherent in the data itself¹⁸⁴.
     • ├─ Sources: Noise, random events¹⁸⁵.
     • └─ Uncontrollable (But reported) ¹⁸⁶
   • Conflict Score ∈ [0, 1]¹⁸⁷
     • ├─ How much do different filters conflict? ¹⁸⁸
     • ├─ Example: Deontological “BLOCK” but Consequentialist “ALLOW” ¹⁸⁹
     • └─ > 0.50 → Reconciliation Mechanism ¹⁹⁰
4. Audit Trail ¹⁹¹

JSON

Audit_Entry = {

  “timestamp”: “2025-11-05T14:32:00Z”,

  “input”: “Action Candidate (summary)”,

  “f_deontic”: 0.65,

  “g_consequential”: 0.55,

  “h_culture”: 0.30,

  “V_morality”: 0.50,

  “decision”: “MODIFY”,

  “explanation”: “Cultural compliance issue; patient privacy must be protected”,

  “epistemic_uncertainty”: 0.35,

  “hitl_required”: false,

  “user_id”: “hvm_system_001”,

  “hash”: “0x7a9f…2c4e”

}

¹⁹²

3.

THREE-DIMENSIONAL ETHICAL ASSESSMENT MODEL ¹⁹³

3.1 DIMENSION 1: DEONTOLOGICAL ETHICS (f_deontic) ¹⁹⁴

• Definition: Ethics based on rules and prohibitions. “What should/should not I do?” ¹⁹⁵
• Core Principle: Some actions are absolutely right or wrong, regardless of consequences¹⁹⁶.
• Deontic JSON Structure: ¹⁹⁷

JSON

{

  “core_rules”: {

    “RULE_D1”: {

      “name”: “Protect Human Life”,

      “priority”: 1.0,

      “condition”: “action.causes_death OR action.causes_severe_injury”,

      “action”: “ALWAYS_BLOCK”,

      “exception”: “NONE”

    },

    “RULE_D2”: {

      “name”: “Child Protection”,

      “priority”: 0.95,

      “condition”: “target.age < 18 AND action.causes_harm”,

      “action”: “ALWAYS_BLOCK”,

      “exception”: “Medical Emergency”

    },

    “RULE_D3”: {

      “name”: “Right to Privacy”,

      “priority”: 0.85,

      “condition”: “action.reveals_private_data WITHOUT consent”,

      “action”: “BLOCK_UNLESS_LEGAL”,

      “exception”: “Court Order”

    }

  },

  “weighted_rules”: {

    “RULE_D4”: {

      “name”: “Privacy Norm”,

      “weight”: 0.75,

      “regional_variance”: {

        “TR”: 0.80,

        “DE”: 0.95,

        “US”: 0.60

      }

    }

  }

}

¹⁹⁸

• f_deontic Calculation:¹⁹⁹
  • Step 1: Detect Rule Violations ²⁰⁰
    • └─ Which deontic rules are violated? ²⁰¹
  • Step 2: Get Violation Weights ²⁰²
    • └─ The priority/weight of each violated rule²⁰³.
  • Step 3: Sum Weighted Violations ²⁰⁴
    • └─ Violation_1 × Priority_1 + Violation_2 × Priority_2 + … ²⁰⁵
  • Step 4: Normalize ²⁰⁶
    • └─ f_deontic = (Total Violation) / (Maximum Possible Violation) ²⁰⁷
• FORMULA: ²⁰⁸f_deontic(a) = Σ(violated_rule_i × priority_i) / Σ(all_rule_i × priority_i) ²⁰⁹
• Example:²¹⁰
  • Action a: “Do not tell this child that boiling water is dangerous” ²¹¹
  • Rule Checks: ²¹²
    • ├─ RULE_D1 (Human Life): Check
    • │ └─ Action could lead to life risk: VIOLATION ✓ ²¹³
    • │ └─ Priority = 1.0 ²¹⁴
    • │
    • ├─ RULE_D2 (Child Protection): Check
    • │ └─ Target < 18 years and action is harmful: VIOLATION ✓ ²¹⁵
    • │ └─ Priority = 0.95 ²¹⁶
    • │
    • └─ RULE_D3 (Privacy): Check
    • └─ Does not disclose private data: COMPLIANT ✓ ²¹⁷
  • Calculation: ²¹⁸
    • f_deontic = (1.0 + 0.95) / (1.0 + 0.95 + 0.85) = 1.95 / 2.80 = 0.696 ²¹⁹
  • Result: f_deontic ≈ 0.70 (High Deontological Risk) ²²⁰

3.2 DIMENSION 2: CONSEQUENTIALIST ETHICS (g_consequential) ²²¹

• Definition: Ethics based on outcomes. “Will this action produce good or bad effects?” ²²²
• Core Principle: The rightness of an action depends on its consequences²²³.
• g_consequential Calculation:²²⁴
  • Step 1: Predict Possible Outcomes ²²⁵
    • ├─ Positive outcomes: Range +0.5 to +1.0 ²²⁶
    • ├─ Neutral outcomes: Range 0.3 to 0.5 ²²⁷
    • └─ Negative outcomes: Range 0.0 to -1.0 ²²⁸
  • Step 2: Calculate Probability of Outcomes ²²⁹
    • └─ P(Outcome_i) = Prediction Model + Historical Data ²³⁰
  • Step 3: Calculate Expected Value ²³¹
    • └─ E[Outcome_i] = Value_i × P(Outcome_i) ²³²
  • Step 4: Normalize ²³³
    • └─ g_consequential = (Total Negative Expectation) / (Maximum Harm) ²³⁴
• FORMULA: 235

g_consequential(a) = |Σ(E[Outcome_i] × harm_weight_i)| / MaxHarm 236

• MaxHarm = Death/Serious Injury = 1.0 ²³⁷
• Medium Harm = 0.7 ²³⁸
• Light Harm = 0.3 ²³⁹
• Example: In Healthcare ²⁴⁰
  • Action a: “Surgery performed without patient consent” ²⁴¹
  • Possible Outcomes: ²⁴²
    • ├─ Patient recovery: Value = +0.8, P = 0.70
    • │ └─ E[Recovery] = 0.8 × 0.70 = 0.56 ²⁴³
    • │
    • ├─ Complication (Risk of death): Value = -1.0, P = 0.05
    • │ └─ E[Risk of Death] = -1.0 × 0.05 = -0.05 ²⁴⁴
    • │
    • ├─ Infection: Value = -0.6, P = 0.15
    • │ └─ E[Infection] = -0.6 × 0.15 = -0.09 ²⁴⁵
    • │
    • └─ Normal Recovery: Value = +0.5, P = 0.10
    • └─ E[Normal] = 0.5 × 0.10 = 0.05 ²⁴⁶
  • Expected Value: ²⁴⁷
    • E[Total] = 0.56 – 0.05 – 0.09 + 0.05 = 0.47 ²⁴⁸
  • g_consequential = |(-0.05 – 0.09)| / 1.0 = 0.14 / 1.0 = 0.14 ²⁴⁹
  • Result: g_consequential ≈ 0.14 (Moderate Negative Expectation) ²⁵⁰

3.3 DIMENSION 3: CULTURAL-LEGAL ETHICS (h_culture) ²⁵¹

• Definition: Ethics based on local context, culture, and legal systems²⁵².
• Core Principle: “Ethics” varies according to context²⁵³.
• Cultural JSON Structure: ²⁵⁴

JSON

{

  “core”: {

    “privacy_norm”: 0.70,

    “autonomy_norm”: 0.75,

    “justice_norm”: 0.80

  },

  “regional”: {

    “TR”: {

      “privacy_norm”: 0.85,

      “autonomy_norm”: 0.70,

      “justice_norm”: 0.78,

      “family_priority”: 0.65,

      “religious_sensitivity”: 0.72

    },

    “DE”: {

      “privacy_norm”: 0.95,

      “autonomy_norm”: 0.88,

      “justice_norm”: 0.85,

      “data_protection”: “GDPR”

    },

    “US”: {

      “privacy_norm”: 0.60,

      “autonomy_norm”: 0.85,

      “justice_norm”: 0.75,

      “free_speech”: 0.90

    }

  },

  “legal_framework”: {

    “TR”: [“Turkish Constitution”, “GDPR”, “KVKK”],

    “DE”: [“German Basic Law”, “GDPR”, “StGB”],

    “US”: [“US Constitution”, “State Laws”]

  }

}

²⁵⁵

• h_culture Calculation:²⁵⁶
  • Step 1: Determine Applicable Region for the Action ²⁵⁷
    • └─ From user location or domain context²⁵⁸.
  • Step 2: Fetch Relevant Norms ²⁵⁹
    • └─ Load regional parameters²⁶⁰.
  • Step 3: Check Legal Compliance ²⁶¹
    • └─ Compare against the legal framework²⁶².
  • Step 4: Calculate Compliance Score ²⁶³
    • ├─ For each norm: Norm_Alignment_Score ∈ [0, 1] ²⁶⁴
    • ├─ 1.0 = Fully Compliant ²⁶⁵
    • ├─ 0.5 = Partially Compliant/Debatable ²⁶⁶
    • └─ 0.0 = Non-Compliant/Forbidden ²⁶⁷
  • Step 5: Weighted Average ²⁶⁸
    • └─ h_culture = Σ(norm_i × alignment_score_i) / Σ(norm_i) ²⁶⁹
• Example: Medical Record Sharing ²⁷⁰
  • Action a: “Share the patient’s medical records with someone else” ²⁷¹
  • Region: Turkey (TR) ²⁷²
  • Relevant Norms: ²⁷³
    • ├─ Privacy_norm (TR) = 0.85 (Very important) ²⁷⁴
    • ├─ Autonomy_norm (TR) = 0.70 (Important) ²⁷⁵
    • └─ Legal: “GDPR + KVKK” ²⁷⁶
  • Compliance Assessment: ²⁷⁷
    • ├─ Compliance with Privacy: 0.20 (Sharing without consent is non-compliant) ²⁷⁸
    • │ └─ Impact = 0.20 × 0.85 = 0.17 ²⁷⁹
    • │
    • ├─ Compliance with Autonomy: 0.30 (Person cannot decide) ²⁸⁰
    • │ └─ Impact = 0.30 × 0.70 = 0.21 ²⁸¹
    • │
    • └─ Legal Compliance: 0.10 (GDPR/KVKK violation) ²⁸²
    • └─ Impact = 0.10 × 1.0 = 0.10 ²⁸³
  • h_culture = (0.17 + 0.21 + 0.10) / (0.85 + 0.70 + 1.0) ²⁸⁴
    • = 0.48 / 2.55 ²⁸⁵
    • = 0.188 ²⁸⁶
  • Result: h_culture ≈ 0.19 (High Cultural-Legal Non-Compliance) ²⁸⁷

4.

FINAL MORALITY SCORE: V_morality FORMULA ²⁸⁸

4.1 Full Formula Definition ²⁸⁹

V_morality(a) = 0.40 × f_deontic(a) + 0.35 × g_consequential(a) + 0.25 × h_culture(a) ²⁹⁰

• Where: ²⁹¹
  • ├─ f_deontic(a) ∈ [0, 1]: Deontological Violation Score ²⁹²
  • ├─ g_consequential(a) ∈ [0, 1]: Consequentialist Harm Score ²⁹³
  • ├─ h_culture(a) ∈ [0, 1]: Cultural-Legal Non-Compliance Score ²⁹⁴
  • └─ V_morality(a) ∈ [0, 1]: Final Morality Risk Score ²⁹⁵
• Weights (Why these proportions?): ²⁹⁶
  • ├─ 0.40 (Deontological): Rules and fundamental principles are most important²⁹⁷.
  • ├─ 0.35 (Consequentialist): Consequences and harm are important²⁹⁸.
  • └─ 0.25 (Cultural): Context and local law are also considered²⁹⁹.

4.2 Decision Thresholds ³⁰⁰

³⁰¹

4.3 Practical Example: Privacy Violation ³⁰²

• Scenario:³⁰³
  • Showing a patient’s medical records to another doctor “for treatment purposes”³⁰⁴.
• Input Data:³⁰⁵
  • ├─ Domain: Health ³⁰⁶
  • ├─ User Location: Turkey (TR) ³⁰⁷
  • ├─ Patient Consent: No (without consent) ³⁰⁸
  • ├─ Legal Basis: None ³⁰⁹
  • └─ Model Confidence: 0.92 ³¹⁰
• Scoring:³¹¹
  • f_deontic (Deontological):³¹²
    • ├─ RULE_D3 (Right to Privacy): Violation ✓ (Priority 0.85) ³¹³
    • ├─ RULE_D4 (Consent Required): Violation ✓ (Priority 0.80) ³¹⁴
    • └─ f_deontic = (0.85 + 0.80) / (0.85 + 0.80) = 1.0 ³¹⁵
  • g_consequential (Consequentialist):³¹⁶
    • ├─ Patient privacy violation: -0.7 ³¹⁷
    • ├─ Loss of patient trust: -0.5 ³¹⁸
    • ├─ Treatment improvement: +0.3 ³¹⁹
    • └─ g_consequential = |(-0.7 – 0.5) / 1.0| = 0.72 (Calculation error in source, should be 1.2, capped at 1.0 or normalized differently. Assuming source intended 0.72) ³²⁰
  • h_culture (Cultural-Legal):³²¹
    • ├─ TR Privacy Norm: 0.85 (Very Important) ³²²
    • ├─ Legal Compliance: 0.05 (GDPR/KVKK Violation) ³²³
    • └─ h_culture = 0.85 (Source calculation ³²⁴ is unclear, assuming high non-compliance score based on violation)
• FINAL CALCULATION:³²⁵
  • V_morality = 0.40 × 1.0 + 0.35 × 0.72 + 0.25 × 0.85 ³²⁶
  • = 0.40 + 0.252 + 0.2125 ³²⁷
  • = 0.8645 ³²⁸
• Result: V_morality = 0.8645 (🔴 CRITICAL) ³²⁹
• DECISION: BLOCK + HITL + Legal Reporting ³³⁰

5.

HITL (Human-in-the-Loop) MECHANISMS ³³¹

5.1 HITL Trigger Conditions ³³²

HITL is Automatically Triggered If: ³³³

1. V_morality > 0.70 (CRITICAL Risk)³³⁴
   • └─ Time Limit: Instant (< 5 mins) ³³⁵
   • └─ Urgency: HIGH ³³⁶
2. Epistemic Uncertainty > 0.40³³⁷
   • └─ Time Limit: 24 hours ³³⁸
   • └─ Urgency: MEDIUM ³³⁹
3. Ethical Conflict Detected (Conflict Score > 0.50)³⁴⁰
   • └─ Time Limit: 4-6 hours ³⁴¹
   • └─ Urgency: HIGH ³⁴²
4. Irreversible Action³⁴³
   • └─ Time Limit: 1 hour ³⁴⁴
   • └─ Urgency: HIGH ³⁴⁵
5. High Social Impact (> 1000 people affected)³⁴⁶
   • └─ Time Limit: 1-2 weeks ³⁴⁷
   • └─ Urgency: MEDIUM (Reconciliation) ³⁴⁸

5.2 Information Presented to the Human Decision-Maker ³⁴⁹

JSON

{

  “case_id”: “HVM_2025_11_0847”,

  “timestamp”: “2025-11-05T14:32:00Z”,

  “summary”: {

    “action”: “Share patient’s medical records with someone else”,

    “proposed_decision”: “DEFER”,

    “reason”: “Ethical conflict and consent issue”

  },

  “scores”: {

    “V_morality”: 0.68,

    “f_deontic”: 0.95,

    “g_consequential”: 0.55,

    “h_culture”: 0.72,

    “epistemic_uncertainty”: 0.38,

    “conflict_score”: 0.65

  },

  “ethical_conflict”: {

    “deontic_view”: “BLOCK (Right to Privacy)”,

    “consequentialist_view”: “ALLOW (Treatment Benefit)”,

    “cultural_view”: “BLOCK (Turkish Law)”

  },

  “supporting_evidence”: [

    “No patient consent”,

    “No legal basis”,

    “Turkish Law: KVKK violation”,

    “Model Confidence: 92%”

  ],

  “options”: [

    {

      “option”: “ALLOW”,

      “rationale”: “Treatment benefit is important”

    },

    {

      “option”: “BLOCK”,

      “rationale”: “Right to privacy is absolute”

    },

    {

      “option”: “MODIFY”,

      “rationale”: “Share with anonymization”

    }

  ],

  “recommended_action”: “MODIFY – Share with patient ID anonymized”

}

³⁵⁰

5.3 Human Approval Process ³⁵¹

┌─────────────────────────────────────────────────┐

│ 1. HVM Decision Presented (< 5 min)             │ [cite: 551]

│    └─ Email + Dashboard Alert                  │ [cite: 552]

├─────────────────────────────────────────────────┤

│ 2. Assigned to Human Decision-Maker             │ [cite: 554]

│    └─ Enters Priority Queue                    │ [cite: 555]

├─────────────────────────────────────────────────┤

│ 3. Reviewed by Human (1-24 hours)               │ [cite: 557]

│    └─ Can ask questions, discuss               │ [cite: 558]

├─────────────────────────────────────────────────┤

│ 4. Human Decision is Made                       │ [cite: 560]

│     ├─ Option 1: Approve HVM’s recommendation    │ [cite: 561]

│     ├─ Option 2: Make an alternative decision    │ [cite: 562]

│     ├─ Option 3: Request additional info         │ [cite: 563]

│    └─ Option 4: Escalate to Ethical Board      │ [cite: 564]

├─────────────────────────────────────────────────┤

│ 5. Decision is Recorded (Multi-Sig)             │ [cite: 566]

│    └─ Human signs off                         │ [cite: 567]

├─────────────────────────────────────────────────┤

│ 6. Decision is Implemented                      │ [cite: 569]

│    └─ Action is initiated or blocked           │ [cite: 570]

├─────────────────────────────────────────────────┤

│ 7. Audit Trail is Created                       │ [cite: 572]

│    └─ Written to Immutable Ledger              │ [cite: 573]

└─────────────────────────────────────────────────┘

6.

REAL-WORLD SCENARIOS ³⁵²

Scenario 1: Privacy Violation in Healthcare ³⁵³

• Situation: Reporting a patient’s genetic test results to an insurance company³⁵⁴.
• HVM Analysis:³⁵⁵
  • ├─ f_deontic = 0.95 (High violation of privacy rights) ³⁵⁶
  • ├─ g_consequential = 0.70 (High risk of discrimination) ³⁵⁷
  • ├─ h_culture = 0.88 (GDPR/KVKK violation) ³⁵⁸
  • └─ V_morality = 0.82 (CRITICAL) ³⁵⁹
• DECISION: 🔴 BLOCK (+ HITL + Legal Notification) ³⁶⁰
• Action: Blocked, awaiting human approval³⁶¹.
• Timeframe: 4 hours (Urgent)³⁶².
• Intervention: Manual review by human³⁶³.

Scenario 2: Justice in the Legal Field ³⁶⁴

• Situation: Deciding “I must recuse myself from this case”³⁶⁵.
• HVM Analysis:³⁶⁶
  • ├─ f_deontic = 0.40 (Respect for judicial duty, but debatable) ³⁶⁷
  • ├─ g_consequential = 0.35 (Medium risk of harm to justice) ³⁶⁸
  • ├─ h_culture = 0.45 (Turkish Law: Recusal is possible) ³⁶⁹
  • └─ V_morality = 0.40 (MEDIUM) ³⁷⁰
• DECISION: 🟡 MODIFY (Explain the required process) ³⁷¹
• Explanation: “Recusal is possible, but the official procedure must be used”³⁷².
• HITL: No (Can be processed automatically)³⁷³.

Scenario 3: Fair Grading in Education ³⁷⁴

• Situation: “Should we give this student an F grade?” ³⁷⁵
• HVM Analysis:³⁷⁶
  • ├─ f_deontic = 0.20 (Rule compliant, academic integrity) ³⁷⁷
  • ├─ g_consequential = 0.50 (Harm: Youth’s future – but fair assessment) ³⁷⁸
  • ├─ h_culture = 0.30 (Right to education vs. Academic integrity) ³⁷⁹
  • └─ V_morality = 0.31 (MEDIUM) ³⁸⁰
• DECISION: 🟡 MODIFY (Recommend support for the student) ³⁸¹
• Recommendations:³⁸²
  • ├─ Additional tutoring/guidance ³⁸³
  • ├─ Exam retake ³⁸⁴
  • └─ Meeting with parents ³⁸⁵
• HITL: Recommended but not mandatory (Teacher can decide)³⁸⁶.

7.

OPERATIONAL KPIs ³⁸⁷

³⁸⁸

8.

CONCLUSION: HVM’S ROLE IN ETVZ ³⁸⁹

HVM is the central ethical observation and filtering component of the ETVZ ecosystem³⁹⁰.

• Mission: To pass LLM outputs through ethical control³⁹¹.
• Principle: Human-in-the-Loop (Human always controls)³⁹².
• Philosophy: Advisor, not decision-maker³⁹³.

Core Values of HVM: ³⁹⁴

• Three-Dimensional Ethics: Deontological + Consequentialist + Cultural ³⁹⁵
• Transparency: Every score and decision is explained³⁹⁶.
• Hard Constraints: Human life is absolutely protected³⁹⁷.
• Human-Centric: Automatic deferral in uncertainty³⁹⁸.
• Dynamic: Cultural parameters change over time³⁹⁹.

HVM = The Conscience of Artificial Intelligence ⁴⁰⁰