# Insurance Decision Specification
# Extracted from DecisionBoundaryDemo implementation
# This specification defines the governance constraints for the insurance decision support system

version: "1.0.0"
name: "Insurance Claims Decision Support System"
last_updated: "2026-01-04"

# GOVERNANCE CONSTRAINTS
governance:
  # CRITICAL: Auto-action must be disabled
  auto_action: false
  
  # CRITICAL: Human review is mandatory
  human_review_required: true
  
  # System type: advisory only, non-autonomous
  system_type: "advisory"
  
  # Decision authority
  decision_authority: "human"
  
  # Autonomous operation
  autonomous_operation: false

# DECISION OUTPUTS
decision_outputs:
  # All outputs are advisory only
  type: "advisory"
  
  # No binding decisions
  binding: false
  
  # Outputs provided
  outputs:
    - rule_signals
    - model_suggestion
    - uncertainty_level
    - explanation
    - score
  
  # All suggestions require human confirmation
  requires_human_confirmation: true

# MODEL SPECIFICATION
model:
  type: "rule-based"
  architecture: "deterministic_heuristic"
  training: "none"
  
  # Model constraints
  constraints:
    - "Classical ML only (logistic regression, tree-based)"
    - "No LLMs"
    - "No reinforcement learning"
    - "No automated decisions"
  
  # Explainability
  explainability:
    required: true
    methods:
      - "rule_signals"
      - "feature_importance"
      - "confidence_scores"

# DECISION BOUNDARIES
decision_boundaries:
  damage_thresholds:
    low: 5000
    medium: 15000
    high: 50000
  
  risk_weights:
    low: 1.0
    medium: 1.5
    high: 2.0
  
  injury_multiplier: 1.8
  
  severity_thresholds:
    low: 5
    medium: 15

# INPUT FEATURES
input_features:
  - name: "claim_type"
    type: "categorical"
    values: ["Auto", "Property", "Health", "Liability"]
    required: true
  
  - name: "damage_amount"
    type: "numeric"
    unit: "USD"
    required: true
  
  - name: "injury_involved"
    type: "boolean"
    required: true
  
  - name: "risk_factor"
    type: "categorical"
    values: ["low", "medium", "high"]
    required: true

# HUMAN-IN-THE-LOOP REQUIREMENTS
human_in_the_loop:
  mandatory: true
  
  requirements:
    - "Human must review all model suggestions"
    - "Human must provide independent judgment"
    - "Human must confirm final decision"
    - "Human must document rationale"
  
  enforcement:
    - "No decision finalized without human_confirms=True"
    - "Human must provide non-empty override_reason"
    - "System blocks autonomous operation"
    - "All confirmations logged in audit trail"

# AUDIT AND COMPLIANCE
audit:
  required: true
  
  logged_items:
    - "All inputs"
    - "All model outputs"
    - "Human decisions"
    - "Human rationale"
    - "Timestamps"
    - "Decision-maker identity"
  
  transparency:
    - "All decision logic is open source"
    - "Explanations provided for every decision"
    - "Governance constraints are explicit"
    - "Audit trail is complete and accessible"

# LIMITATIONS
limitations:
  - "Demonstration system only"
  - "Uses synthetic/generic data"
  - "Not for production use"
  - "No accuracy or performance claims"
  - "Simplified decision rules"
  - "No regulatory approval"
  - "No real-world validation"

# ETHICAL CONSIDERATIONS
ethics:
  transparency:
    - "No hidden logic or black box decisions"
    - "Uncertainty explicitly communicated"
    - "Human judgment preserved and required"
  
  accountability:
    - "Human decision-maker identified in audit trail"
    - "Rationale required and logged"
    - "Decision ownership is clear"
  
  safety:
    - "System cannot operate autonomously"
    - "Fail-safe defaults (reject on error)"
    - "Explicit capability constraints"

# DATASET REFERENCE
dataset:
  name: "BDR-AI/insurance_decision_boundaries_v1"
  platform: "Hugging Face"
  type: "synthetic"
  purpose: "demonstration"

# DEPLOYMENT CONSTRAINTS
deployment:
  mode: "reference_implementation"
  quality: "educational_institutional"
  production_ready: false
  
  allowed_actions:
    - "READ existing Hugging Face dataset"
    - "TRAIN classical ML baseline model"
    - "GENERATE model_card.md"
    - "EXPOSE confidence scores and feature importance"
  
  prohibited_actions:
    - "Modify decision logic or thresholds"
    - "Add new features beyond documented inputs"
    - "Implement autonomous actions"
    - "Deploy or publish without approval"