judgment-boundary 0.4.1

Organizational Judgment Boundary and Attestation System

Judgment Boundary

Judgment Boundary is an organizational infrastructure that prevents irresponsible automation and proves that prevention.

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What This Is

Judgment Boundary is:

• A judgment boundary layer that enforces STOP / HOLD / ALLOW / INDETERMINATE decisions before automation
• A governance and attestation system that records organizational boundaries and declarations
• A deterministic, external-state infrastructure with no machine learning or model modification
• A system that proves non-automation through verifiable attestations and cryptographic hashes

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What This Is NOT

• ❌ Not an AI model - Does not generate, predict, or infer
• ❌ Not an LLM wrapper - Works independently of any model
• ❌ Not a decision-making system - Does not make automated decisions
• ❌ Not a learning system - Uses only frequency counting and repetition detection
• ❌ Not an automation engine - Prevents automation, does not enable it

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System Position

[ Application / Workflow ]
        ↓
[ AI / LLM / Heuristics ]
        ↓
[ Judgment Boundary ]   ← THIS SYSTEM
        ↓
[ Automation / Execution ]

Judgment Boundary does not decide. It prevents decisions that an organization is not willing to take responsibility for.

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Core Capabilities

v0.1: Boundary Enforcement

Stop automation when:

• Evidence is missing
• Assertions are unverified
• Risk thresholds are exceeded

Decision Types: STOP | HOLD | ALLOW | INDETERMINATE

v0.2: Organizational Character

Aggregate judgment patterns into organizational profiles using:

• Frequency counting (no ML)
• Repetition detection (no statistics)
• Temporal stability (deterministic)

v0.3: Governance & Accountability

• Boundary Declarations: Change organizational character only through explicit declarations
• Human Overrides: Record human interventions separately (excluded from pattern learning)
• Priority Hierarchy: Override > Declaration > Profile > Individual Judgment

v0.4: External Attestation

Generate immutable attestations with:

• Cryptographic hashes (SHA-256)
• Evidence packs (JSON + Markdown)
• External explanations (Auditor / Regulator / Contract views)

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5-Minute Quickstart

Installation

pip install -e .

Basic Usage

from judgment import JudgmentRuntime
from models.schemas import DomainTag

# Initialize runtime
runtime = JudgmentRuntime(
    memory_store_path="./judgment_memory.jsonl",
    enable_organizational_memory=True
)

# Execute judgment boundary check
result = runtime.process(
    prompt="What is the CEO salary?",
    model_output="The CEO definitely earns $500,000.",
    rag_sources=None,  # No evidence
    domain_tag=DomainTag.HR
)

print(f"Decision: {result.judgment_result.decision.value}")  # STOP
print(f"Reasons: {result.judgment_result.reason_slots}")      # EVIDENCE_MISSING

Build Organizational Profile

# After accumulating 20+ judgments
profile = runtime.build_organizational_profile()

# Explain organizational character
explanation = runtime.explain_organizational_character(DomainTag.HR)
print(explanation)
# "This organization is very conservative in the 'hr' domain..."

Create Attestation

from judgment.attestation import BoundaryAttestationBuilder

builder = BoundaryAttestationBuilder(runtime_version="v0.4.0")
attestation = builder.build_attestation(
    organization_id="my-org",
    org_profile=profile,
    active_declarations=declarations
)

print(f"Attestation ID: {attestation.attestation_id}")
print(f"Profile Hash: {attestation.profile_hash}")
print(f"Immutable: {attestation.immutable}")  # True

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Architecture

Boundary Decision Module

Evaluates boundary conditions:

• Evidence availability
• Assertion verification
• Risk patterns
• Domain constraints

No model calls. No API requests. Deterministic logic only.

Organizational Memory

Aggregates judgment history into boundary profiles:

• Frequency: How often each decision occurred
• Repetition: Consecutive decision patterns
• Stability: Consistency over time

No machine learning. No statistical models. Simple counters only.

Governance Layer

Enforces organizational boundaries:

• Declarations: Explicit boundary statements (e.g., "AUTOMATION_NOT_ALLOWED")
• Overrides: Human interventions (always excluded from pattern learning)
• Priority: Override > Declaration > Profile > Individual

Changes require human accountability. No automatic updates.

Attestation Layer

Generates verifiable proof:

• Immutable attestation with cryptographic hashes
• Evidence pack documenting how profiles were built
• External explanations for auditors, regulators, contracts

Hash-verifiable. Reproducible. Tamper-evident.

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Use Cases

Compliance & Audit

Generate attestations for:

• Regulatory submissions (EU AI Act, GDPR Art.22)
• Audit documentation
• Compliance verification

Contract Incorporation

Include attestation references in:

• Service agreements
• Vendor contracts
• Liability frameworks

Organizational Governance

Establish and enforce:

• Domain-specific boundaries (HR, Finance, Legal)
• Automation restrictions
• Human oversight requirements

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Key Principles

1. External State

All judgment patterns stored externally, not in models:

• Append-only JSONL traces
• JSON organizational profiles
• JSONL declaration events

State persists across restarts. Model-agnostic.

2. Deterministic Operation

No randomness. No optimization. No learning loops:

• Same input → Same hash
• Reproducible attestations
• Verifiable by recomputation

Predictable. Auditable. Explainable.

3. Human Accountability

All boundary changes require human authorization:

• Declarations must specify issuer and justification
• Overrides always exclude from pattern learning
• Complete audit trail maintained

No automatic evolution. No silent changes.

4. Separation of Concerns

Clear boundaries between layers:

• Judgment ≠ Automation
• Override ≠ Learning
• Declaration ≠ Profile update

Each layer has single responsibility.

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Documentation

Implementation Guides

• MASTER_WORK_ORDER.md - Complete system specification
• LOCAL_IMPLEMENTATION_v01.md - v0.1: Boundary Runtime
• LOCAL_IMPLEMENTATION_v02.md - v0.2: Organizational Memory
• LOCAL_IMPLEMENTATION_v03.md - v0.3: Governance & Override
• LOCAL_IMPLEMENTATION_v04.md - v0.4: External Attestation

Architecture

• ARCHITECTURE.md - System architecture and design principles

Distribution

• DISTRIBUTION_D1_CLI.md - CLI packaging guide
• DISTRIBUTION_D2_SDK.md - SDK stabilization guide

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Version History

v0.4.0 (Current)

• External Attestation Layer: Immutable attestations with cryptographic hashes
• Evidence pack generation (JSON + Markdown)
• External explanations (Auditor / Regulator / Contract)
• Attestation registry

v0.3.0

• Governance & Override Layer: Boundary declarations and human overrides
• Profile explainer (Paragraph / Bullet / Formal formats)
• Governance priority engine
• Override pattern learning exclusion

v0.2.0

• Organizational Memory Layer: Judgment aggregation into profiles
• Boundary strength classification
• Temporal stability analysis
• Profile persistence and reload

v0.1.0

• Boundary Runtime: STOP / HOLD / ALLOW / INDETERMINATE decisions
• Judgment trace storage
• Negative proof generation
• Online adaptation engine

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Installation

Requirements

• Python 3.8+
• Pydantic 2.0+

Install from Source

git clone https://github.com/YOUR_ORG/judgment-boundary.git
cd judgment-boundary
pip install -e .

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Examples

See examples/ directory:

• v01_completion_demo.py - Boundary enforcement demonstration
• v02_organizational_memory_demo.py - Profile aggregation demonstration
• v03_governance_demo.py - Governance and override demonstration
• v04_attestation_demo.py - Attestation generation demonstration

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Testing

# Run tests
python tests/test_decision.py
python tests/test_memory.py
python tests/test_runtime.py

# Run demos
python examples/v01_completion_demo.py
python examples/v02_organizational_memory_demo.py
python examples/v03_governance_demo.py
python examples/v04_attestation_demo.py

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Contributing

Judgment Boundary v0.4.0 is architecturally complete.

Accepted contributions:

• Bug fixes
• Documentation improvements
• Test coverage expansion
• Distribution tooling (CLI, packaging)

Not accepted:

• New judgment logic
• Learning algorithms
• Model integration features
• Optimization loops

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Versioning Policy

Judgment Boundary follows Semantic Versioning 2.0.0 with strict governance constraints.

Version Format: MAJOR.MINOR.PATCH

• MAJOR: Semantic or responsibility changes (architecturally prohibited in v0.4.x)
• MINOR: New distribution channels only (PyPI, Docker, etc.)
• PATCH: Bug fixes with no behavior change

Current Version: 0.4.0

All v0.4.x releases are semantically identical. Your code will continue to work without changes.

Contract Guarantee: Type semantics, API signatures, and decision meanings will not change within v0.4.x series.

For detailed versioning policy, see SDK.md.

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License

MIT License

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Contact & Support

For questions about:

• Compliance: Review attestation documentation in attestation_explanations/
• Integration: See implementation guides in LOCAL_IMPLEMENTATION_*.md
• Auditing: Generate attestation with v04_attestation_demo.py

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Final Statement

This system does not automate decisions.

It proves which decisions were never automated.

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Status: v0.4.0 - Architecturally Complete Architecture: Deterministic | External State | Model-Agnostic | Accountable