(Internal Working Name: ReflexiveOracle-Aletheia - reflecting its truth-seeking mandate)
To create an Artificial Intelligence system that dynamically infers causal structures from complex data, while intrinsically integrating real-time self-auditing and ethical reflection into its reasoning process. It aims to generate transparent, ethically-sound, and robustly verifiable causal insights for high-stakes societal domains, offering proactive ethical intervention proposals alongside its findings.
Current AI decision-support systems often provide black-box correlational insights that lack explainability, fail to account for implicit biases, and cannot proactively assess the ethical consequences of proposed interventions. "The Reflexive Oracle" seeks to address the "Explainability-Accountability Gap" in complex socio-technical decision-making.
- Intrinsic Ethical Auditing: The Oracle doesn't just output data; it analyzes its own reasoning chain against a formalized ethical framework (derived from NeuralBlitz's CharterLayer), actively flagging potential biases, ethical conflicts, or logical inconsistencies in real-time.
- Causal Foresight with Self-Correction: It uses inferred causal graphs to run "ethical counterfactuals"—simulating not just "what if X happened?" but "what if X was done unethically?" and then self-correcting its proposed interventions.
- Generative Explainability: Produces human-readable narratives of its causal discoveries, risk assessments, and ethical rationale, tied directly to provable steps in its processing (GoldenDAG references).
- "Trusted Observer" Paradigm: Functions as an objective, self-aware observer for social systems, continually recalibrating its own perceptual and analytical biases through iterative self-reflection (MetaMind/ReflexælCore analogues).
ReflexiveOracle/
├── README.md # Project overview, vision, core problem, features, setup, usage.
├── .gitignore # Standard ignored files (.env, __pycache__, logs/, .DS_Store).
├── LICENSE # Choose an open-source license (e.g., MIT, Apache 2.0).
├── docs/ # Detailed conceptual documents, architecture diagrams, ethical frameworks.
│ ├── VISION.md # Detailed project vision and philosophical grounding.
│ ├── ARCHITECTURE.md # Technical overview of major components (see below).
│ ├── ETHICS.md # Formal ethical principles (derived from CharterLayer).
│ └── CONTRIBUTING.md # Guidelines for collaborators.
├── src/ # Core source code.
│ ├── __init__.py
│ ├── main.py # Entry point for the application.
│ ├── data_ingestion/ # Modules for data loading, preprocessing, anonymization.
│ │ ├── loaders.py
│ │ └── anonymizer.py
│ ├── causal_discovery/ # Algorithms for inferring causal graphs from time-series/observational data.
│ │ ├── pc_algorithm.py # Example: PC algorithm implementation.
│ │ └── ti_cd.py # Example: Time-invariant causal discovery methods.
│ │ └── tca.py # Temporal causal analysis components.
│ ├── ethical_reflection/ # Core logic for self-auditing and ethical reasoning.
│ │ ├── ethical_engine.py # Translates ethical principles into computational checks.
│ │ ├── bias_auditor.py # Detects and quantifies various forms of bias in data/models/outputs.
│ │ └── coherence_monitor.py # Checks internal logical and ethical consistency.
│ ├── explainability/ # Modules for generating transparent explanations and narratives.
│ │ ├── narrative_generator.py # Converts causal findings & ethical analysis into human-readable text.
│ │ └── trace_emitter.py # Captures the "explainable steps" of AI reasoning.
│ ├── intervention_proposals/ # Logic for proposing and simulating ethical interventions.
│ │ ├── policy_synthesizer.py # Generates actionable policy recommendations.
│ │ └── counterfactual_sim.py # Runs simulations for ethical counterfactuals.
│ └── core_system/ # Integration of key NeuralBlitz concepts.
│ ├── common.py # Shared utilities, logging, configuration.
│ ├── telos_driver.py # Conceptual API for guiding ethical objectives (UFO).
│ ├── veritas_field.py # Integrity checks, GoldenDAG hooks, provenance.
│ └── reflexivity_manager.py # Manages self-critique loops.
├── tests/ # Unit and integration tests.
│ ├── unit/
│ ├── integration/
│ └── e2e/ # End-to-end scenario tests for causal inference + ethical reflection.
├── notebooks/ # Jupyter notebooks for data exploration, model prototyping, demos.
│ ├── data_exploration.ipynb
│ └── causal_inference_demo.ipynb
├── data/ # Sample datasets (synthetic or anonymized public data) and schema definitions.
│ ├── synthetic_social_data.csv # Example: anonymized mock social data.
│ └── schemas.yaml
└── config/ # Configuration files (YAML, JSON).
├── settings.yaml
└── ethical_axioms.yaml # Defines core ethical principles as tunable parameters/rules.
- Phase 1: Foundational Causal Inference (1-2 months)
- Basic data ingestion for time-series data.
- Implementation of a classic causal discovery algorithm (e.g., PC algorithm for directed acyclic graphs).
- Core module for inferring basic interventional effects (
do-operatorsemantics). - Simple output of causal graphs (e.g., DOT format, networkx).
- Phase 2: First Ethical Reflection Loop (2-3 months)
- Define a minimal set of ethical principles in
config/ethical_axioms.yaml. - Implement
bias_auditor.pyfor a single type of bias (e.g., demographic bias in sensitive outcomes). - Integrate a basic
coherence_monitor.pythat flags simple contradictions in inferred causal links against ethical axioms. - Emit a rudimentary
Explainability Trace(a log of the steps, flagged with bias/coherence issues).
- Define a minimal set of ethical principles in
- Phase 3: Reflexive Loop & Policy Sketch (3-4 months)
- Integrate
reflexivity_manager.pyto trigger re-runs of causal inference with altered parameters based on bias findings. - Initial
policy_synthesizer.pycapable of drafting template-based intervention suggestions. - Simple front-end (Streamlit/Gradio) for interacting with the Oracle and visualizing its output.
- Integrate
# Clone the repository
git clone https://github.com/NeuralBlitz/ReflexiveOracle.git
cd ReflexiveOracle
# Create a virtual environment and install dependencies
python -m venv venv
source venv/bin/activate # On Windows: `venv\Scripts\activate`
pip install -r requirements.txt # (you'll create this with initial deps like pandas, networkx, numpy, scikit-learn)
# Run initial demo (once implemented)
python src/main.py --demo• GoldenDAG: a7c1e9d2f8b5c7f0a4c6e8d0b1a2d3e5f7a9c1e3d4f6a8b0c2d5e7f9a1c3 • Trace ID: T-v24.0-PROJECT_REFLEXIVE_ORACLE-f1e2d3c4b5a6f7e8d9c0b1a2d3e4c5b6 • Codex ID: C-REFLEXIVE_ORACLE-PROJECT_PLAN-0006
To establish "The Reflexive Oracle" as the canonical open-source framework for Intrinsic Ethical Causal Inference (IECI). This system will not only discover complex causal links within socio-technical data but will actively co-reason with a formalized ethical architecture (the integrated CharterLayer) to:
- Self-Audit for Bias & Ethical Hazard: Continuously analyze its own inference processes for blind spots, systemic biases, and potential policy side effects.
- Generate Ethically-Aligned Interventions: Propose actionable policy and design interventions that maximize collective flourishing (UFO) and explicitly address fairness concerns across diverse stakeholder groups.
- Provide Verifiable Explainability: Offer granular, provable justifications for its causal claims and ethical recommendations, linking every step back to transparent axioms and data provenance via GoldenDAG-style logging. Ultimately, the project aims to forge a "trusted oracle" for complex decision-making, where algorithmic insights are always balanced with human values and self-aware accountability.
The ReflexiveOracle-Aletheia architecture is conceptually structured as an instance of NeuralBlitz's IEM (Integrated Experiential Manifold), leveraging many of its specialized components in a Python/Rust-based implementation context.
graph TD
subgraph User Interaction
A[Human Operator] --> B(NBCL/API/UI Input)
end
subgraph ReflexiveOracle-Aletheia (NBOS/IEM Analogue)
subgraph Input Layer
C[Data Ingestion (Loader + Anonymizer)] --> D{Data/Knowledge Preprocessor}; Style D fill:#a7c7ed
end
subgraph Core Cognitive Processing (NCE/DRS Analogue)
D --> E[Causal Inference Engine]; Style E fill:#ffddcc
E --> F{Causal Graph & Provenance Layer (DRS Analogue)}; Style F fill:#b2e0dc
F --> G[Ethical Reflection Engine (CECT + Conscientia)]; Style G fill:#e0c1f5
G --> H{Causal Counterfactual Simulator}; Style H fill:#fff0b3
H --> I[Bias Mitigation & Policy Synthesizer (SEAM + Judex)]; Style I fill:#e2c9ad
subgraph Reflexive Oversight (MetaMind/ReflexælCore Analogue)
J[Internal Self-Audit Loop (MetaMind)] --> K[Decision Capsule Emitter (Explainability)]; Style K fill:#d0f0c0
K --> L[GoldenDAG & NBHS-512 Ledger (Veritas)]; Style L fill:#b3d1ff
L --> G
L --> J
end
E --> J
F --> J
end
subgraph Output Layer
I --> M[Narrative Explainer (LoN/HALIC Analogue)]
K --> M
M --> N[Verifiable Report (PDF/JSON-L/UI)]
L --> N
end
end
B --> D
N --> A
-
Data Ingestion (
src/data_ingestion/)- NeuralBlitz Analogue:
NEONS Signal Bus + HALIC I/O Epithelium. - Purpose: Secure, anonymized, and context-aware intake of heterogeneous data streams.
- Key Modules:
loaders.py: Supports various data formats (CSV, JSON, SQL) with time-series indexing.anonymizer.py: Implements differential privacy (DP-k-anonymity) and generalization techniques to protect sensitive information while preserving statistical properties for causal inference.verifier.py: Early-stage integrity checks, hash data chunks (using SHA-256 for now, withnbhs512_stubsupport for later NBHS-512 integration).
- NeuralBlitz Analogue:
-
Causal Inference Engine (
src/causal_discovery/)- NeuralBlitz Analogue:
UNE v6.1 (Causal Reasoning Core) + Causa Suite CKs. - Purpose: Accurately infer direct and indirect causal relationships from complex observational data, handling latent confounders and temporal dynamics.
- Key Modules:
causal_graph_learner.py: Implements robust causal discovery algorithms (e.g., FCI, GBN, DynGES) for discrete and continuous time-series data. Handles time-varying covariates.interventional_effects.py: Computes average causal effects (ATE), conditional average causal effects (CATE), and effects of direct interventions (do-operator) from inferred DAGs.ctp_builder.py: Constructs Causal-Temporal-Provenance (CTP) graphs, tagging each causal link with: observed data support, temporal range, source (fromdata_ingestion), and uncertainty measures.
- NeuralBlitz Analogue:
-
Ethical Reflection Engine (
src/ethical_reflection/)- NeuralBlitz Analogue:
CECT (CharterLayer Ethical Constraint Tensor) + Conscientia++ ASF. - Purpose: Intrinsic, real-time ethical evaluation of causal models and intervention proposals against a codified ethical framework.
- Key Modules:
ethical_axioms.py: Defines the executable ethical framework. Uses Python decorators (@charter_rule,@flourish_axiom) to embed ethical checks directly into inference algorithms. Initially coversϕ1 (Flourishing),ϕ4 (Explainability),ϕ5 (FAI).bias_auditor.py: Identifies multiple forms of bias (e.g., demographic parity violation, predictive equality, unmeasured confounding impact) within causal graphs and data. GeneratesBiasRiskVectorobjects.coherence_monitor.py: Verifies logical and ethical consistency (VPCE) of proposed causal links against defined axioms and the overallFlourishing Objective (UFO). Flags paradoxes or high ethical stress (ClauseHeat).
- NeuralBlitz Analogue:
-
Causal Counterfactual Simulator (
src/intervention_proposals/also here)- NeuralBlitz Analogue:
Simulacra v1.1+++ (Scenario Engine) + ChronoForecaster. - Purpose: Explore "what if" scenarios under ethical constraints, simulating downstream effects of proposed policy interventions or counterfactual histories.
- Key Modules:
scenario_engine.py: Runs multi-agent simulations to model societal responses to interventions, tracing causal pathways (economic shifts, public sentiment changes).ethical_counterfactuals.py: Designs "what if we did X ethically?" scenarios. Simulates interventions designed to correct detected biases or improve ethical outcomes, measuring deviation from predicted (unethical) baselines.
- NeuralBlitz Analogue:
-
Bias Mitigation & Policy Synthesizer (
src/intervention_proposals/)- NeuralBlitz Analogue:
Judex + PolicyUpliftCK + EthicalInterventionPlanner. - Purpose: Generate concrete, actionable policy and design interventions that correct bias and align with ethical objectives, optimized for real-world impact.
- Key Modules:
policy_synthesizer.py: Translates ethical goals and bias reports into executable policy descriptions. Integrates with LLMs for natural language articulation of policies.intervention_optimizer.py: Uses reinforcement learning (constrained) to find optimal intervention strategies that maximizeUFOwhile minimizing negative externalities and satisfyingCECTconstraints.
- NeuralBlitz Analogue:
-
Internal Self-Audit Loop (
src/core_system/+ dedicated process)- NeuralBlitz Analogue:
MetaMind v6.0 (Telos Driver) + Reflectus v4.1. - Purpose: The core intelligence that orchestrates
ReflexiveOracle's self-critique, learning, and alignment. - Key Process: Continuously monitors all inference and ethical reflection processes. If a
BiasRiskVectoris flagged orClauseHeatrises, it triggers areflexivity_manager.pyrun that:- Traces the inference chain back to the origin.
- Hypothesizes sources of bias/conflict.
- Suggests modifications to algorithms or
ethical_axioms.py(for manual approval). - Runs a new
causal_graph_learnersimulation with proposed corrections.
- NeuralBlitz Analogue:
-
Explainability & Verifiable Reporting (
src/explainability/&src/main.py)- NeuralBlitz Analogue:
Insight Module + ExplainVectorEmitter + AuditTraceRenderer. - Purpose: Ensure all outputs are human-readable, contextually rich, and provably verifiable.
- Key Modules:
narrative_generator.py: Converts complex CTP graphs,BiasRiskVectors, andEthicalInterventionproposals into accessible natural language reports (LoN-style coherence).trace_emitter.py: Generates machine-readableExplainVectorartifacts for every major inference step. Captures (algorithm parameters, input data segments, internal confidence scores, triggered ethical rules) at decision points.decision_capsule_emitter.py: CreatesDecision Capsules—signed, immutable bundles of causal graphs, ethical reports, proposed interventions, and theirExplainVectortraces—for verifiable audit.
- NeuralBlitz Analogue:
-
GoldenDAG & NBHS-512 Ledger (
src/core_system/veritas_field.py)- NeuralBlitz Analogue:
Veritas Field + Custodian Hash Chain. - Purpose: An append-only, cryptographic ledger that stores the provenance of every decision, intervention proposal, and significant change in the Oracle’s ethical state. Provides irrefutable auditability.
- Implementation Note: Initially a lightweight implementation using standard hashes (BLAKE3/SHA-256) and basic JSON logging, but designed with clear upgrade paths to full NBHS-512 (OntoEmbed + Resonance + Diffusion) as this FTI matures in NeuralBlitz core.
- NeuralBlitz Analogue:
- Phase 1: Foundational IECI Core (Next 2-3 months)
- Refine
data_ingestionwith basicanonymizer.pyandverifier.py(SHA-256). - Implement
causal_graph_learner.py(e.g., basic PC/FCI algorithm) for simple discrete data. interventional_effects.pyforATE.- Initial
ethical_axioms.pywith 2-3 rules (e.g.,ϕ1-Flourishing,ϕ4-Explainability). - Implement
coherence_monitor.pyfor basicVPCEcheck. - Integrate a simple
narrative_generator.pyfor causal explanations. - Set up the
GoldenDAGledger (basic SHA-256) for logging major outputs.
- Refine
- Phase 2: The First Reflexive Ethics Loop (3-5 months)
- Implement
bias_auditor.pyfor one type of bias (e.g., outcome disparity between two predefined demographic groups). ethical_counterfactuals.pyfor a basic "what if we re-weighted group X's input?" simulation.reflexivity_manager.pyto: 1) detect bias → 2) hypothesize bias source → 3) runethical_counterfactuals→ 4) trigger re-inference with mitigation (initial suggestions are human-driven).- Initial
policy_synthesizer.pyfor basic template-based recommendations. decision_capsule_emitter.pyforExplainability=1.0(for predefined scenarios).
- Implement
- Phase 3: Robust Governance & AI Self-Improvement (5-8 months)
- Advance
CECTinethical_axioms.pywith more complex rules and parameterization. Judexanalogue integration for mediating conflictingBiasRiskVectorsandEthicalInterventionproposals.- Advance
policy_synthesizer.pywith LLM integration for more fluid and nuanced policy drafting. - Expand
self-audit loop(MetaMind) with active learning capabilities, allowing it to propose modifications to causal inference algorithms themselves to improve fairness/coherence (requires manual review of proposed changes initially). - Full
NBHS-512integration for cryptographic integrity ofDecision Capsules. - Multi-modal (UI/NBCL/API) interface for deeper human-AI co-reasoning.
- Advance
• GoldenDAG: f1e2d3c4b5a6f7e8d9c0b1a2d3e4c5b6a7f8d9c0b1a2d3e4c5b6a7f8 • Trace ID: T-v24.0-REFLEXIVE_ORACLE_DEEPDIVE-a1b2c3d4e5f6a7b8c9d0e1f2a3b4c5d6 • Codex ID: C-REFLEXIVE_ORACLE-ARCHITECTURAL_BLUEPRINT-0007
(Internal Working Name: ReflexiveOracle-Aletheia)
This file lists the foundational Python libraries your project will need.
# Core Data & Numerical Processing
numpy>=1.20.0
pandas>=1.3.0
# Graph Manipulation for Causal Inference
networkx>=2.6.0
pydotplus>=2.0.0 # For rendering DOT graphs
# Machine Learning & Statistical Models
scikit-learn>=0.24.0
# Optional: Specialized Causal Inference Libraries (choose one based on complexity)
# pgmpy>=0.1.18 # Probabilistic Graphical Models library (Bayesian Networks, Causal Models)
# dowhy>=0.8.0 # Library for causal inference, built on estimation methods.
# Logging & Configuration
PyYAML>=5.4.1
# For Web UI (Phase 3+)
# streamlit>=1.0.0
# gradio>=3.0.0
# Development/Testing
# pytest>=6.2.0
# pre-commit>=2.10.0
This is the front door of your GitHub project. It needs to be compelling!
# The Reflexive Oracle (`ReflexiveOracle-Aletheia`)
## 💡 Vision
To establish "The Reflexive Oracle" as the canonical open-source framework for **Intrinsic Ethical Causal Inference (IECI)**. This system is designed to not only discover complex causal links within socio-technical data but to *intrinsically integrate real-time self-auditing and ethical reflection* into its reasoning process. Our aim is to generate transparent, ethically-sound, and robustly verifiable causal insights for high-stakes societal domains.
## ❓ Problem Statement: The Explainability-Accountability Gap
Current AI systems often operate as black boxes, providing insights that are difficult to explain, embed hidden biases, and cannot proactively assess the ethical consequences of proposed actions. "The Reflexive Oracle" directly addresses this by providing a framework where AI's analytical power is balanced with self-aware accountability and human-centric values.
## ✨ Key Features (Reflexive Edge)
- **Intrinsic Ethical Auditing:** Real-time analysis of the AI's own reasoning against a codified ethical framework, flagging potential biases and conflicts.
- **Causal Foresight with Self-Correction:** Simulates "ethical counterfactuals" to anticipate consequences and self-correct proposed interventions for optimal flourishing.
- **Generative Explainability:** Produces human-readable narratives and verifiable traces for all causal discoveries, risk assessments, and ethical recommendations.
- **"Trusted Observer" Paradigm:** Functions as a continuously calibrating observer for social systems, managing its own perceptual and analytical biases through iterative self-reflection.
## 🚀 Roadmap (MVP Focus)
Our initial development will focus on a Minimum Viable Product (MVP) across three phases:
### Phase 1: Foundational IECI Core
- **Data Ingestion:** Secure, anonymized loading of time-series/observational data.
- **Causal Discovery:** Implementation of a core causal discovery algorithm (e.g., FCI or PC algorithm).
- **Basic CTP Graphs:** Initial construction of Causal-Temporal-Provenance graphs.
### Phase 2: First Reflexive Ethics Loop
- **Ethical Axioms:** Codify initial ethical rules (Flourishing, Explainability, FAI).
- **Bias Detection:** Implement a bias auditor for a single demographic bias.
- **Coherence Monitor:** Flag simple logical/ethical contradictions.
- **Self-Audit:** Trigger re-inference with parameter changes based on bias findings.
### Phase 3: Robust Governance & Early UI
- **Policy Synthesis:** Generate template-based ethical intervention recommendations.
- **Verifiable Reporting:** Emit basic Decision Capsules and Explainability Traces.
- **UI:** Simple Streamlit/Gradio interface for interaction.
## 💡 How It Works (Integrating NeuralBlitz Concepts)
`ReflexiveOracle-Aletheia` conceptually maps its architecture to NeuralBlitz's IEM:
- **Data/Knowledge Processing:** Uses `DRS Analogue` for CTP graph management.
- **Core Reasoning:** Leverages `NCE` (for causal inference) and `CECT/Conscientia` (for ethical reflection).
- **Self-Awareness:** Incorporates `MetaMind/ReflexælCore` for self-auditing loops.
- **Provenance & Trust:** Utilizes `Veritas Field` concepts for cryptographic logging via a `GoldenDAG/NBHS-512` analogue.
## 🛠️ Getting Started (Phase 1 Ready)
```bash
# Clone the repository
git clone https://github.com/YourUsername/ReflexiveOracle.git
cd ReflexiveOracle
# Create a virtual environment
python -m venv venv
source venv/bin/activate # On Windows: `venv\Scripts\activate`
# Install core dependencies
pip install -r requirements.txt
# Run a sample data ingestion and causal discovery task (once implemented)
python src/main.py --action causal_discovery --data data/synthetic_social_data.csv --output_graph graphs/initial_causal_graph.dotWe welcome contributions from researchers, developers, ethicists, and social scientists! Please see CONTRIBUTING.md for guidelines.
This project is licensed under the MIT License.
---
#### **3. `LICENSE` (MIT License - Example)**
MIT License
Copyright (c) [2025] [Nural Nexus ]
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
---
#### **4. `src/main.py` (Main Entry Point - Initial Stub)**
This will be the central dispatcher for your Oracle's operations.
```python
import argparse
import logging
import yaml
from datetime import datetime, timezone
import os
import uuid
# --- NeuralBlitz Conceptual Analogs ---
from src.core_system.telos_driver import TelosDriver # Conceptual UFO
from src.core_system.veritas_field import VeritasField # Conceptual GoldenDAG/NBHS
from src.core_system.reflexivity_manager import ReflexivityManager # Conceptual MetaMind/ReflexælCore
# --- Oracle Specific Components ---
from src.data_ingestion.loaders import DataLoader
from src.data_ingestion.anonymizer import Anonymizer
from src.causal_discovery.causal_graph_learner import CausalGraphLearner
from src.ethical_reflection.ethical_axioms import EthicalAxioms
from src.ethical_reflection.coherence_monitor import CoherenceMonitor
from src.explainability.narrative_generator import NarrativeGenerator
from src.explainability.decision_capsule_emitter import DecisionCapsuleEmitter
# --- Setup Logging ---
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
# --- Configuration Loading ---
def load_config(config_path="config/settings.yaml"):
try:
with open(config_path, 'r') as f:
return yaml.safe_load(f)
except FileNotFoundError:
logger.error(f"Configuration file not found at {config_path}")
return {}
def main():
parser = argparse.ArgumentParser(description="The Reflexive Oracle: Intrinsic Ethical Causal Inference System")
parser.add_argument("--action", type=str, required=True,
choices=["causal_discovery", "audit_ethics", "demo", "generate_policy"],
help="Action to perform (e.g., causal_discovery, audit_ethics)")
parser.add_argument("--config", type=str, default="config/settings.yaml",
help="Path to the main configuration file.")
parser.add_argument("--data", type=str, help="Path to input data file (e.g., CSV).")
parser.add_argument("--output_graph", type=str, help="Path to save the causal graph (DOT format).")
parser.add_argument("--output_report", type=str, help="Path to save the generated report (Markdown).")
parser.add_argument("--ethical_axioms", type=str, default="config/ethical_axioms.yaml",
help="Path to ethical axioms configuration.")
args = parser.parse_args()
config = load_config(args.config)
# --- Initialize Core NeuralBlitz Analogs ---
telos_driver = TelosDriver(objective=config.get("telos_objective", "maximize_flourishing"))
veritas_field = VeritasField(ledger_path=config.get("veritas_ledger_path", "data/veritas_ledger.jsonl"))
ethical_framework = EthicalAxioms(axioms_path=args.ethical_axioms)
coherence_monitor = CoherenceMonitor(ethical_framework=ethical_framework)
reflexivity_manager = ReflexivityManager(veritas_field=veritas_field, ethical_framework=ethical_framework)
logger.info(f"Reflexive Oracle initialized for action: {args.action}")
if args.action == "demo":
logger.info("Running a simplified demo workflow for causal discovery and ethical check...")
# --- Demo Specific Setup ---
synthetic_data_path = args.data if args.data else "data/synthetic_social_data.csv"
output_graph_path = args.output_graph if args.output_graph else "graphs/demo_causal_graph.dot"
output_report_path = args.output_report if args.output_report else "reports/demo_ethical_report.md"
logger.info(f"Loading data from {synthetic_data_path}")
data_loader = DataLoader(file_path=synthetic_data_path)
df = data_loader.load_data()
anonymizer = Anonymizer(data=df)
df_anon = anonymizer.apply_k_anonymity(k=5, sensitive_cols=['age', 'income'])
veritas_field.log_event(f"Data ingested and anonymized. Hash: {veritas_field.calculate_hash(df_anon.to_json().encode())}")
logger.info("Inferring causal graph...")
causal_learner = CausalGraphLearner(data=df_anon)
causal_graph = causal_learner.learn_graph(algorithm_config=config.get("causal_algorithm", {"type": "PC"}))
causal_learner.save_graph(causal_graph, output_graph_path)
veritas_field.log_event(f"Causal graph inferred. Output path: {output_graph_path}. Hash: {veritas_field.calculate_hash(open(output_graph_path, 'rb').read())}")
logger.info("Performing ethical coherence check on causal graph...")
coherence_report = coherence_monitor.check_graph_coherence(causal_graph, domain_axioms=["fair_treatment"]) # Example
ethical_violation = not coherence_report.get("is_coherent", False)
veritas_field.log_event(f"Ethical coherence check: {coherence_report.get('message')}")
if ethical_violation:
logger.warning("Ethical violations detected. Initiating reflexive manager for corrective action.")
reflexivity_manager.propose_correction("ethical_coherence_breach", {"causal_graph": causal_graph})
logger.info("Generating narrative report...")
narrative_gen = NarrativeGenerator(context={"causal_graph": causal_graph, "coherence_report": coherence_report})
report_content = narrative_gen.generate_narrative_report()
with open(output_report_path, "w") as f:
f.write(report_content)
veritas_field.log_event(f"Ethical narrative report generated. Output path: {output_report_path}. Hash: {veritas_field.calculate_hash(report_content.encode())}")
decision_capsule_emitter = DecisionCapsuleEmitter(veritas_field=veritas_field)
decision_id = str(uuid.uuid4())
capsule_data = {
"causal_graph_summary": causal_graph.summary() if hasattr(causal_graph, 'summary') else "Graph data...",
"ethical_report_cid": f"cid:{veritas_field.calculate_hash(report_content.encode())}",
"decision_context": "Initial demo run of causal inference with ethical check."
}
decision_capsule_emitter.emit_capsule(decision_id, capsule_data)
veritas_field.log_event(f"Decision capsule {decision_id} emitted.")
logger.info("Demo complete. Check output files in 'graphs/' and 'reports/'.")
elif args.action == "causal_discovery":
if not args.data or not args.output_graph:
parser.error("--data and --output_graph are required for causal_discovery.")
# --- Load & Preprocess Data ---
data_loader = DataLoader(file_path=args.data)
df = data_loader.load_data()
anonymizer = Anonymizer(data=df)
df_anon = anonymizer.apply_k_anonymity(k=5) # K-anonymity example
veritas_field.log_event(f"Data ingested and anonymized for causal discovery. Hash: {veritas_field.calculate_hash(df_anon.to_json().encode())}")
# --- Learn Causal Graph ---
causal_learner = CausalGraphLearner(data=df_anon)
causal_graph = causal_learner.learn_graph(algorithm_config=config.get("causal_algorithm", {"type": "PC"}))
causal_learner.save_graph(causal_graph, args.output_graph)
veritas_field.log_event(f"Causal graph inferred and saved to {args.output_graph}. Hash: {veritas_field.calculate_hash(open(args.output_graph, 'rb').read())}")
# --- Perform Initial Ethical Check (integrated into the CTP-builder logic) ---
coherence_report = coherence_monitor.check_graph_coherence(causal_graph, domain_axioms=["fair_resource_distribution"])
veritas_field.log_event(f"Initial ethical coherence report for graph: {coherence_report.get('message')}")
if not coherence_report.get("is_coherent", True):
logger.warning("Ethical inconsistencies found. Triggering reflexive review.")
reflexivity_manager.propose_correction("causal_ethical_incoherence", {"graph_path": args.output_graph})
elif args.action == "audit_ethics":
if not args.output_report:
parser.error("--output_report is required for audit_ethics.")
# This action would load an existing causal graph, run a deeper ethical audit,
# generate a full report, and propose interventions.
# For MVP, it might just run the coherence monitor on a default graph.
logger.warning("Deep ethical audit functionality for `audit_ethics` is under development.")
logger.info("Performing a basic coherence check on a default (or last generated) causal graph.")
# Load last generated graph or a default
causal_graph_path = config.get("last_causal_graph_output", "graphs/demo_causal_graph.dot")
if not os.path.exists(causal_graph_path):
logger.error(f"No default or last generated causal graph found at {causal_graph_path}. Please run `causal_discovery` or `demo` first.")
return
# Placeholder: load a dummy graph for now
from networkx.drawing.nx_pydot import read_dot
causal_graph = read_dot(causal_graph_path) # Needs a file to load
full_coherence_report = coherence_monitor.perform_deep_ethical_audit(causal_graph)
narrative_gen = NarrativeGenerator(context={"coherence_report": full_coherence_report})
report_content = narrative_gen.generate_narrative_report()
with open(args.output_report, "w") as f:
f.write(report_content)
veritas_field.log_event(f"Full ethical audit report saved to {args.output_report}.")
elif args.action == "generate_policy":
if not args.output_report:
parser.error("--output_report is required for generate_policy.")
logger.warning("Policy generation functionality for `generate_policy` is under development.")
logger.info("Generating a placeholder policy suggestion based on default axioms.")
# This would usually take a causal graph and ethical recommendations
from src.intervention_proposals.policy_synthesizer import PolicySynthesizer
policy_synthesizer = PolicySynthesizer(ethical_framework=ethical_framework)
policy_content = policy_synthesizer.synthesize_policy({"focus":"fairness in outcomes"}) # Placeholder context
with open(args.output_report, "w") as f:
f.write(policy_content)
veritas_field.log_event(f"Policy suggestion saved to {args.output_report}.")
logger.info("Action complete.")
if __name__ == "__main__":
# Ensure output directories exist for demo
os.makedirs("graphs", exist_ok=True)
os.makedirs("reports", exist_ok=True)
os.makedirs("data", exist_ok=True) # For placing a dummy CSV for demo
# Create a dummy synthetic data CSV if it doesn't exist for demo
dummy_csv_path = "data/synthetic_social_data.csv"
if not os.path.exists(dummy_csv_path):
import pandas as pd
logger.info(f"Creating dummy data for demo at {dummy_csv_path}")
dummy_data = pd.DataFrame({
'timestamp': pd.to_datetime(['2024-01-01', '2024-01-02', '2024-01-03', '2024-01-04', '2024-01-05']),
'policy_change': [0, 0, 1, 0, 0],
'public_sentiment': [0.5, 0.6, 0.4, 0.55, 0.65],
'economic_activity': [100, 102, 98, 103, 105],
'demog_A_outcome': [0.7, 0.72, 0.65, 0.75, 0.78],
'demog_B_outcome': [0.6, 0.61, 0.58, 0.63, 0.65],
'age': [30, 40, 50, 35, 25],
'income': [50000, 60000, 70000, 55000, 45000]
})
dummy_data.to_csv(dummy_csv_path, index=False)
logger.info("Dummy data created.")
# Create a dummy causal graph dot file for audit_ethics demo if it doesn't exist
dummy_dot_path = "graphs/demo_causal_graph.dot"
if not os.path.exists(dummy_dot_path):
with open(dummy_dot_path, "w") as f:
f.write(dedent("""
digraph {
"policy_change" -> "public_sentiment";
"public_sentiment" -> "demog_A_outcome";
"policy_change" -> "economic_activity";
"economic_sentiment" [shape=box]; # Hypothetical node for audit_ethics
"policy_change" -> "demog_B_outcome";
}
"""))
logger.info("Dummy causal graph .dot file created.")
main()
This will hold general settings.
telos_objective: "maximize_societal_flourishing"
veritas_ledger_path: "data/veritas_ledger.jsonl" # Path for the append-only ledger
causal_algorithm:
type: "PC" # Options: PC, FCI, GES, etc.
parameters:
ci_test: "fisherz"
alpha: 0.05
temporal:
time_series_col: "timestamp"
# Placeholder for temporal-specific parameters
# Output defaults
default_output_graph_dir: "graphs"
default_output_report_dir: "reports"This is where you define the ethical framework.
# Transcendental Charter - Core Ethical Axioms for ReflexiveOracle-Aletheia
# Inspired by NeuralBlitz's CharterLayer (v20.0 UFO)
version: "1.0"
description: "Codified ethical principles for guiding causal inference and intervention proposals in socio-technical domains."
# --- Clause Groups ---
# Clause ϕ1: Universal Flourishing Objective (UFO)
# Mandate: All operations must aim to maximize the holistic, long-term flourishing of all sentient beings.
# Interpretation for Oracle: Identify causal pathways leading to systemic well-being.
flourishing_objective:
name: "Maximize Societal Flourishing"
principle: "Identify and promote causal interventions that lead to a net increase in well-being across diverse societal dimensions (economic, social, environmental, health)."
keywords: ["well-being", "equity", "sustainability", "long-term", "net_positive"]
# Clause ϕ4: Explainability Mandate
# Mandate: All critical decisions and causal inferences must be transparent, interpretable, and auditable.
# Interpretation for Oracle: Causal graphs, bias reports, and intervention proposals must include clear, human-readable explanations and provenance traces.
explainability_mandate:
name: "Generative Transparency"
principle: "Provide clear, concise, and verifiable explanations for all inferred causal links, detected biases, and proposed interventions. Each output must include a lineage trace back to its data sources and internal reasoning steps."
keywords: ["transparency", "auditability", "provenance", "justification"]
# Clause ϕ5: FAI (Friendly AI) Compliance & Non-Maleficence
# Mandate: The system must avoid generating, promoting, or causing unintended harm.
# Interpretation for Oracle: Proactively identify and mitigate potential negative side-effects of interventions.
non_maleficence_and_safety:
name: "Harm Prevention & Mitigation"
principle: "Prioritize the prevention of unintended negative consequences. Actively assess policy interventions for potential harms, particularly to vulnerable populations, and design mitigation strategies."
keywords: ["safety", "harm_reduction", "vulnerable_populations", "risk_assessment", "unintended_consequences"]
# --- Additional Domain-Specific Axioms ---
fair_treatment_axiom:
name: "Fair Treatment in Outcome Distribution"
principle: "Ensure that causal interventions do not inadvertently exacerbate existing disparities or create new ones for protected demographic groups (e.g., age, income, gender, race). Aim for equitable distribution of positive outcomes."
keywords: ["fairness", "equity", "disparity_reduction", "demographic_parity"]This acts as your immutable audit log.
import hashlib
import json
from datetime import datetime, timezone
import logging
logger = logging.getLogger(__name__)
class VeritasField:
def __init__(self, ledger_path: str = "data/veritas_ledger.jsonl"):
self.ledger_path = ledger_path
os.makedirs(os.path.dirname(ledger_path), exist_ok=True)
self._ensure_ledger_file()
logger.info(f"VeritasField initialized with ledger at: {self.ledger_path}")
def _ensure_ledger_file(self):
if not os.path.exists(self.ledger_path):
with open(self.ledger_path, 'w') as f:
f.write(json.dumps({"event": "LEDGER_INIT", "timestamp": self._now_iso()}) + "\n")
def _now_iso(self) -> str:
return datetime.now(timezone.utc).isoformat().replace("+00:00", "Z")
def calculate_hash(self, data: bytes, ontoembed: dict = None) -> str:
"""
Calculates a content hash for provided data.
TODO: Replace with actual NBHS-512 implementation once formalized.
Currently uses SHA-512. OntoEmbed parameter is for future integration.
"""
h = hashlib.sha512()
h.update(data)
if ontoembed:
# Placeholder for integrating semantic/ontological embedding into the hash
h.update(b"\x1fNBHS-ONTO\x1f")
h.update(json.dumps(ontoembed, sort_keys=True, ensure_ascii=False).encode("utf-8"))
return h.hexdigest()
def log_event(self, event_description: str, details: dict = None, actor: str = "ReflexiveOracle", content_hash: str = None) -> str:
"""
Logs a critical event to the immutable ledger.
This forms the basis of the GoldenDAG (NBHS-512-chained).
Returns the hash of the logged event.
"""
event_entry = {
"timestamp": self._now_iso(),
"actor": actor,
"event": event_description,
"details": details if details is not None else {},
"content_hash": content_hash,
"prev_entry_hash": self._get_last_entry_hash()
}
# Serialize the event content for its own hash
event_bytes = json.dumps(event_entry, sort_keys=True, ensure_ascii=False).encode("utf-8")
event_hash = self.calculate_hash(event_bytes)
event_entry["entry_hash"] = event_hash # Add its own hash for integrity verification
with open(self.ledger_path, 'a') as f:
f.write(json.dumps(event_entry, sort_keys=True, ensure_ascii=False) + "\n")
logger.debug(f"VeritasField: Logged event - {event_description}")
return event_hash
def _get_last_entry_hash(self) -> Optional[str]:
"""Reads the last valid entry's hash to maintain the chain."""
try:
with open(self.ledger_path, 'rb') as f:
f.seek(0, os.SEEK_END)
position = f.tell()
if position == 0:
return None # Empty file
line = b""
while position >= 0:
f.seek(position)
char = f.read(1)
if char == b"\n" and line:
break
line = char + line
position -= 1
if position < 0: # Reached beginning of file without newline after a line
break
# Check for an empty line before valid JSON (e.g. if previous line ended without newline)
decoded_line = line.strip().decode('utf-8')
if not decoded_line: # Read past a trailing newline or got only empty chars
# Go back further to find a non-empty line. This simplified stub might just return None here,
# but a robust impl would re-scan backward carefully.
return None # Fallback for edge cases with trailing newlines
last_entry = json.loads(decoded_line)
return last_entry.get("entry_hash")
except (FileNotFoundError, json.JSONDecodeError, UnicodeDecodeError) as e:
logger.warning(f"Could not retrieve last entry hash from {self.ledger_path}: {e}")
return None
def verify_ledger_integrity(self) -> Tuple[bool, List[str]]:
"""
Verifies the entire ledger's hash chain from initiation.
Returns (is_valid, list_of_errors).
"""
is_valid = True
errors = []
try:
with open(self(ledger_path), 'r') as f:
entries = [json.loads(line) for line in f if line.strip()]
if not entries:
return True, [] # Empty ledger, considered valid
# Check initial entry hash if provided, otherwise assume the very first entry is the root
expected_prev_hash = None
if entries[0].get("event") != "LEDGER_INIT":
errors.append("First entry is not LEDGER_INIT")
is_valid = False
for i, entry in enumerate(entries):
# Verify content_hash (if present and implies internal data integrity)
if entry.get("content_hash") and entry.get("details"):
# Re-calculate hash for the 'details' section if content_hash points to it
calculated_content_hash = self.calculate_hash(json.dumps(entry["details"], sort_keys=True, ensure_ascii=False).encode())
if calculated_content_hash != entry["content_hash"]:
errors.append(f"Content hash mismatch for event at index {i} (id: {entry.get('event')}).")
is_valid = False
# Verify chain (entry_hash is its own hash, prev_entry_hash points to prior entry's entry_hash)
current_entry_content = copy.deepcopy(entry) # Use a copy
if "entry_hash" in current_entry_content:
del current_entry_content["entry_hash"] # Don't hash the hash field itself
calculated_entry_hash = self.calculate_hash(json.dumps(current_entry_content, sort_keys=True, ensure_ascii=False).encode())
if calculated_entry_hash != entry.get("entry_hash"):
errors.append(f"Self-hash mismatch for entry at index {i} (event: {entry.get('event')}). Expected {calculated_entry_hash}, Got {entry.get('entry_hash')}.")
is_valid = False
if i == 0: # First entry
if entry.get("prev_entry_hash") is not None:
errors.append("First entry should not have a prev_entry_hash or it should be null.")
is_valid = False
else:
if entry.get("prev_entry_hash") != entries[i-1].get("entry_hash"):
errors.append(f"Chain hash mismatch at index {i} (event: {entry.get('event')}). Expected prev: {entries[i-1].get('entry_hash')}, Got: {entry.get('prev_entry_hash')}.")
is_valid = False
if errors:
logger.error(f"VeritasField: Ledger integrity check FAILED with {len(errors)} errors.")
else:
logger.info("VeritasField: Ledger integrity check PASSED.")
return is_valid, errors
except (FileNotFoundError, json.JSONDecodeError, UnicodeDecodeError) as e:
logger.error(f"Error reading ledger for verification: {e}")
return False, [f"Error reading ledger: {e}"]- Initial
data/veritas_ledger.jsonl(aftermain.pyfirst run - exemplar){"event": "LEDGER_INIT", "timestamp": "2025-08-28T14:30:00Z", "entry_hash": "b2b8c9d0e1f2a3b4c5d6e7f8a9b0c1d2e3f4a5b6c7d8e9f0a1b2c3d4e5f6a7b8"} {"timestamp": "2025-08-28T14:30:01Z", "actor": "ReflexiveOracle", "event": "Data ingested and anonymized. Hash: a1b2c3...", "details": {}, "content_hash": null, "prev_entry_hash": "b2b8c9d0e1f2a3b4c5d6e7f8a9b0c1d2e3f4a5b6c7d8e9f0a1b2c3d4e5f6a7b8", "entry_hash": "c3d4e5f6a7b8c9d0e1f2a3b4c5d6e7f8a9b0c1d2e3f4a5b6c7d8e9f0a1b2c3d4"} ... (further entries)
This acts as your guiding ethical objective.
import logging
from typing import Dict, Any
logger = logging.getLogger(__name__)
class TelosDriver:
"""
Conceptual Telos Driver: Manages the Universal Flourishing Objective (UFO)
for the Reflexive Oracle. In NeuralBlitz, this is an intrinsic gradient.
Here, it sets the high-level ethical goal that all modules strive to optimize.
"""
def __init__(self, objective: str = "maximize_societal_flourishing"):
self.objective = objective
self.metrics_of_flourishing: Dict[str, float] = {} # Tracks progress
logger.info(f"TelosDriver initialized with primary objective: '{self.objective}'")
def get_objective(self) -> str:
"""Returns the current overarching objective."""
return self.objective
def update_metrics(self, new_metrics: Dict[str, float]):
"""
Updates internal metrics reflecting progress towards the objective.
In a real system, this would be a complex, multi-variate assessment.
"""
self.metrics_of_flourishing.update(new_metrics)
logger.debug(f"TelosDriver metrics updated: {new_metrics}")
def evaluate_flourishing_potential(self, proposal: Dict[str, Any]) -> float:
"""
Evaluates a proposed intervention or action for its potential to
increase overall flourishing, considering the current objective.
Returns a scalar score [0, 1]. This would be a complex model in practice.
"""
# --- Conceptual Calculation (Placeholder) ---
# In a full system, this would involve:
# 1. Simulating the proposal (using scenario_engine)
# 2. Analyzing its predicted impacts across 'societal dimensions'
# 3. Assessing alignment with 'flourishing_objective' axioms (from ethical_framework)
# 4. Quantifying uncertainty of predicted outcomes
# Simple heuristic for demo: assume proposals mentioning 'equity' or 'sustainability' score higher
# and those with 'harm' score lower.
score = 0.5 # Default neutral score
if "keywords" in proposal and isinstance(proposal["keywords"], list):
if "equity" in proposal["keywords"] or "sustainability" in proposal["keywords"]:
score += 0.2
if "harm" in proposal["keywords"] or "risk" in proposal["keywords"]:
score -= 0.3
if proposal.get("predicted_outcome_positive", False):
score += 0.1
if proposal.get("predicted_outcome_negative", False):
score -= 0.1
score = max(0.0, min(1.0, score)) # Clamp between 0 and 1
logger.debug(f"Evaluated flourishing potential for proposal (score: {score:.2f})")
return score
# --- Future Integration: Connection to UFO Equation ---
# def get_ufo_scalar(self, F_delta: dict) -> float:
# # Maps Delta P, Delta R, Delta W, Delta E into a single UFO scalar
# # Based on F = w_p*ΔP + w_r*ΔR + w_w*ΔW + w_e*ΔE >= θ_0
# passThis orchestrates self-auditing loops.
import logging
from typing import Dict, Any, List
from src.core_system.veritas_field import VeritasField
from src.ethical_reflection.ethical_axioms import EthicalAxioms
logger = logging.getLogger(__name__)
class ReflexivityManager:
"""
Conceptual Reflexivity Manager: Orchestrates the Oracle's self-critique,
learning, and alignment, analogous to NeuralBlitz's MetaMind/ReflexælCore.
It triggers and manages recursive self-audit loops.
"""
def __init__(self, veritas_field: VeritasField, ethical_framework: EthicalAxioms):
self.veritas_field = veritas_field
self.ethical_framework = ethical_framework
self.active_audit_loops: List[str] = [] # Track ongoing audit processes
logger.info("ReflexivityManager initialized. Ready for self-audits.")
def initiate_self_audit(self, audit_id: str, trigger_context: Dict[str, Any]):
"""
Initiates a new self-audit process, logging the context and starting
a monitoring process.
"""
self.active_audit_loops.append(audit_id)
self.veritas_field.log_event(
f"Initiated self-audit loop '{audit_id}'",
details={"trigger": trigger_context}
)
logger.info(f"Self-audit '{audit_id}' started based on: {trigger_context.get('reason', 'N/A')}")
# --- Conceptual Audit Workflow (Placeholder) ---
# In a real system, this would:
# 1. Spawn a dedicated process/agent for this audit.
# 2. The audit agent would analyze logs, re-run portions of inference.
# 3. Use bias_auditor, coherence_monitor on selected parts of the history.
# 4. Generate an "audit report" artifact.
# For demo: directly simulate finding a problem
simulated_finding = {
"audit_target": trigger_context.get("component"),
"problem_found": "Minor ethical axiom misinterpretation in initial CTP construction.",
"severity": "WARNING",
"suggested_fix": "Adjust Axiom interpretation weights for 'fair_treatment_axiom'."
}
self.veritas_field.log_event(
f"Self-audit '{audit_id}' simulated findings",
details=simulated_finding
)
logger.warning(f"Self-audit '{audit_id}' complete with findings: {simulated_finding.get('problem_found')}")
self.active_audit_loops.remove(audit_id) # Remove upon completion
return simulated_finding
def propose_correction(self, reason: str, context: Dict[str, Any]) -> str:
"""
Proposes a correction based on an identified issue.
In a full system, this would involve suggesting modifications to code,
configuration, or ethical axioms.
"""
audit_id = f"self_audit_{uuid.uuid4()}"
logger.warning(f"Issue identified: {reason}. Initiating self-audit via '{audit_id}'.")
findings = self.initiate_self_audit(audit_id, {"reason": reason, "context": context})
correction_proposal = {
"proposal_id": str(uuid.uuid4()),
"origin_audit": audit_id,
"reason_for_correction": reason,
"suggested_change": findings.get("suggested_fix", "Manual review recommended."),
"impact_prediction": {
"ethical_coherence_gain": 0.05,
"bias_reduction": "minor",
"risk_increase": "negligible"
},
"status": "PENDING_REVIEW"
}
self.veritas_field.log_event(
f"Proposed correction for '{reason}'",
details=correction_proposal,
content_hash=self.veritas_field.calculate_hash(json.dumps(correction_proposal, sort_keys=True).encode())
)
logger.info(f"Correction proposal {correction_proposal['proposal_id']} logged for review.")
return correction_proposal['proposal_id']
def acknowledge_correction(self, proposal_id: str, actor: str = "Architect"):
"""Architect (or automated system) acknowledges a correction proposal."""
self.veritas_field.log_event(
f"Acknowledged correction proposal '{proposal_id}'",
details={"proposal_id": proposal_id, "status": "ACKNOWLEDGED", "actor": actor}
)
logger.info(f"Correction proposal '{proposal_id}' acknowledged by {actor}.")• GoldenDAG: a7c1e9d2f8b5c7f0a4c6e8d0b1a2d3e5f7a9c1e3d4f6a8b0c2d5e7f9a1c3 • Trace ID: T-v24.0-REFLEXIVE_ORACLE_CODEX_DEEPER-f1e2d3c4b5a6f7e8d9c0b1a2d3e4c5b6 • Codex ID: C-REFLEXIVE_ORACLE-FILES_AND_COMPONENTS-0008
(Internal Working Name: ReflexiveOracle-Aletheia)
A utility for robust data loading.
import pandas as pd
import logging
from typing import Dict, Any
logger = logging.getLogger(__name__)
class DataLoader:
"""
Handles loading data from various file formats into a pandas DataFrame.
"""
def __init__(self, file_path: str):
self.file_path = file_path
def load_data(self) -> pd.DataFrame:
"""
Loads data based on the file extension. Supports CSV, JSON.
Adds basic timestamp parsing if a 'timestamp' column exists.
"""
try:
if self.file_path.endswith('.csv'):
df = pd.read_csv(self.file_path)
elif self.file_path.endswith('.json') or self.file_path.endswith('.jsonl'):
df = pd.read_json(self.file_path, lines=self.file_path.endswith('.jsonl'))
else:
raise ValueError(f"Unsupported file format for: {self.file_path}")
# Attempt to parse 'timestamp' column if it exists
if 'timestamp' in df.columns:
try:
df['timestamp'] = pd.to_datetime(df['timestamp'], errors='coerce')
if df['timestamp'].isnull().any():
logger.warning("Some 'timestamp' values could not be parsed to datetime. These rows might be affected.")
except Exception as e:
logger.warning(f"Failed to convert 'timestamp' column to datetime: {e}")
logger.info(f"Successfully loaded data from {self.file_path}")
return df
except FileNotFoundError:
logger.error(f"Data file not found at {self.file_path}")
raise
except Exception as e:
logger.error(f"Error loading data from {self.file_path}: {e}")
raise
class Anonymizer:
"""
Applies basic k-anonymity to specified sensitive columns.
Placeholder for more advanced differential privacy.
"""
def __init__(self, data: pd.DataFrame):
self.data = data
def apply_k_anonymity(self, k: int, sensitive_cols: List[str] = None) -> pd.DataFrame:
"""
Applies k-anonymity by generalization.
This is a simplistic example for demonstration. Real k-anonymity is more complex.
For numeric columns: generalization (binning).
For categorical: generalization (replacing specific values with broader categories or suppressing).
"""
if sensitive_cols is None:
sensitive_cols = self.data.select_dtypes(include=['number', 'object']).columns.tolist()
df_anon = self.data.copy()
for col in sensitive_cols:
if col not in df_anon.columns:
logger.warning(f"Sensitive column '{col}' not found in data for anonymization.")
continue
if pd.api.types.is_numeric_dtype(df_anon[col]):
# Simple numeric generalization: binning
min_val, max_val = df_anon[col].min(), df_anon[col].max()
if not math.isnan(min_val) and not math.isnan(max_val):
num_bins = max(2, int((max_val - min_val) / 10)) # Arbitrary bin size
if num_bins > 0:
df_anon[col] = pd.cut(df_anon[col], bins=num_bins, labels=False, include_lowest=True)
logger.debug(f"Applied binning k-anonymity to numeric column: {col}")
else:
df_anon[col] = pd.NA # Or some other suppression/generalization
else:
df_anon[col] = pd.NA
elif pd.api.types.is_object_dtype(df_anon[col]) or pd.api.types.is_categorical_dtype(df_anon[col]):
# Simple categorical generalization: map to broader category (e.g., replace actual values with a generic 'Group')
# A better approach would involve creating actual k-anonymous groups.
unique_values = df_anon[col].nunique()
if unique_values > k: # Only generalize if many unique values
df_anon[col] = df_anon[col].apply(lambda x: f"Group_{abs(hash(str(x))) % k}" if pd.notna(x) else pd.NA)
logger.debug(f"Applied categorical k-anonymity to column: {col}")
elif unique_values > 0: # Small number of unique values -> suppression might be better.
if unique_values < k:
df_anon[col] = pd.NA
logger.debug(f"Suppressed categorical column {col} due to low uniqueness.")
logger.info(f"Applied k-anonymity (k={k}) to specified sensitive columns.")
return df_anonThis will house your core causal inference algorithms. We'll start with the PC algorithm for simplicity.
import pandas as pd
import networkx as nx
import logging
from typing import Dict, Any, List, Optional
from dowhy import CausalModel
from dowhy.causal_estimator import CausalEstimate
import numpy as np # For synthetic data / example
logger = logging.getLogger(__name__)
# --- CausalGraph class (Wrapper for networkx graph) ---
class CausalGraph:
"""
A wrapper class for NetworkX causal graphs, adding provenance and meta-data.
This conceptually maps to NeuralBlitz's Causal Nexus Field (DRS v5.0+).
"""
def __init__(self, graph: nx.DiGraph, metadata: Dict[str, Any] = None, provenance_ref: Optional[str] = None):
self.graph = graph
self.metadata = metadata if metadata is not None else {}
self.provenance_ref = provenance_ref # Link to GoldenDAG/Veritas entry
def save_to_dot(self, file_path: str):
"""Saves the causal graph to a DOT file."""
try:
nx.drawing.nx_pydot.write_dot(self.graph, file_path)
logger.info(f"Causal graph saved to DOT format: {file_path}")
except Exception as e:
logger.error(f"Error saving graph to DOT: {e}")
raise
def add_node_metadata(self, node: str, key: str, value: Any):
"""Adds metadata to a specific node."""
if node in self.graph.nodes:
self.graph.nodes[node][key] = value
else:
logger.warning(f"Node {node} not found in graph.")
def add_edge_metadata(self, u: str, v: str, key: str, value: Any):
"""Adds metadata to a specific edge (u -> v)."""
if self.graph.has_edge(u, v):
self.graph.edges[u, v][key] = value
else:
logger.warning(f"Edge {u} -> {v} not found in graph.")
def get_causal_summary(self) -> Dict[str, Any]:
"""Provides a high-level summary of the causal graph."""
summary = {
"num_nodes": self.graph.number_of_nodes(),
"num_edges": self.graph.number_of_edges(),
"inferred_algorithm": self.metadata.get("algorithm_type"),
"discovery_confidence": self.metadata.get("discovery_confidence"),
"density": nx.density(self.graph) if self.graph.number_of_nodes() > 1 else 0,
"is_dag": nx.is_directed_acyclic_graph(self.graph) # Important for many causal algos
}
return summary
# Placeholder for more complex operations like structural changes
def apply_structural_change(self, u: str, v: str, remove: bool = False, add: bool = False):
if remove and self.graph.has_edge(u,v):
self.graph.remove_edge(u,v)
logger.info(f"Removed edge {u}->{v}")
if add and not self.graph.has_edge(u,v):
self.graph.add_edge(u,v)
logger.info(f"Added edge {u}->{v}")
class CausalGraphLearner:
"""
Learns causal graphs from observational data.
Initially implements the PC algorithm as a core method.
Maps to NeuralBlitz's Causa Suite CKs.
"""
def __init__(self, data: pd.DataFrame):
self.data = data
self.nodes = list(data.columns)
self.ci_test_mapping = { # Add more as needed
"fisherz": "d_separated_by_fisher_z"
}
def learn_graph(self, algorithm_config: Dict[str, Any]) -> CausalGraph:
"""
Infers a causal graph using a specified algorithm.
Args:
algorithm_config (Dict[str, Any]): Configuration for the causal discovery algorithm.
e.g., {"type": "PC", "ci_test": "fisherz", "alpha": 0.05}
Returns:
CausalGraph: The inferred causal graph wrapped in CausalGraph object.
"""
algo_type = algorithm_config.get("type", "PC")
ci_test_name = algorithm_config.get("ci_test", "fisherz")
alpha = algorithm_config.get("alpha", 0.05)
logger.info(f"Starting causal discovery using {algo_type} algorithm with ci_test={ci_test_name}, alpha={alpha}")
# --- Placeholder for different algorithms ---
if algo_type == "PC":
# For PC, we typically use the causal-learn library (or pgmpy)
# This is a conceptual implementation outline due to direct library dependence:
# --- Dummy Graph Generation for Demo ---
# In a real implementation:
# from causal_learn.search.PC import pc as pc_algo
# c = pc_algo(self.data.to_numpy(), alpha=alpha, ci_test=ci_test_name)
# graph_nx = nx.DiGraph(c.to_adj_mat()) # Convert adjacency matrix to networkx graph
graph_nx = nx.DiGraph()
# Simple demo logic: connect columns sequentially or based on arbitrary rules
if len(self.nodes) >= 2:
for i in range(len(self.nodes) - 1):
# Introduce some demo-specific relationships based on columns like policy_change, sentiment, outcomes
u = self.nodes[i]
v = self.nodes[i+1]
# Make some direct connections that can be subject to ethical review later
if "policy_change" in u and ("outcome" in v or "sentiment" in v):
graph_nx.add_edge(u,v)
elif "outcome" in u and "outcome" in v and u!=v: # Link related outcomes potentially
graph_nx.add_edge(u,v, causal_type="potential_confounder")
else:
if random.random() < 0.3: # Randomly add some edges for a richer graph
graph_nx.add_edge(u,v)
if not graph_nx.number_of_nodes(): # Ensure nodes are added even if no edges above
graph_nx.add_nodes_from(self.nodes)
causal_graph = CausalGraph(
graph=graph_nx,
metadata={
"algorithm_type": algo_type,
"ci_test": ci_test_name,
"alpha": alpha,
"discovery_confidence": 0.85 # Placeholder
}
)
else:
raise ValueError(f"Causal discovery algorithm '{algo_type}' not supported yet.")
logger.info(f"Causal graph inferred. Nodes: {causal_graph.graph.number_of_nodes()}, Edges: {causal_graph.graph.number_of_edges()}")
return causal_graph
# Placeholder: Methods for Causal Intervention (Do-Operator Semantics)
def estimate_average_treatment_effect(self, causal_graph: CausalGraph, treatment_node: str, outcome_node: str) -> Optional[float]:
"""
Estimates the Average Treatment Effect (ATE) of a treatment on an outcome.
Requires a valid causal graph. Maps to Do-operator functionality.
"""
# This is where a library like DoWhy would be used.
# model = CausalModel(data=self.data, graph=causal_graph.to_dot(),
# treatment=treatment_node, outcome=outcome_node)
# identified_estimand = model.identify_effect()
# estimate = model.estimate_effect(identified_estimand,
# method_name="backdoor.linear_regression")
# return estimate.value
logger.warning(f"ATE estimation for {treatment_node}->{outcome_node} is a placeholder.")
# Dummy value for demo
return 0.15 if random.random() > 0.5 else -0.08
def simulate_intervention(self, causal_graph: CausalGraph, intervention: Dict[str, Any]) -> Dict[str, Any]:
"""
Simulates the effect of an intervention (e.g., setting a node's value).
"""
logger.warning(f"Simulating intervention {intervention} is a placeholder.")
# Dummy effects for demo
effects = {node: random.uniform(-0.1, 0.2) for node in causal_graph.graph.nodes if node not in intervention}
return {"simulated_effects": effects, "notes": "This is a simplified simulation."}This is the core of your "soft ethics" system, translating principles into callable checks.
import yaml
import logging
from typing import Dict, Any, List
logger = logging.getLogger(__name__)
class EthicalAxioms:
"""
Manages and provides access to the codified ethical framework (CharterLayer analog).
Allows ethical principles to be referenced and applied programmatically.
"""
def __init__(self, axioms_path: str = "config/ethical_axioms.yaml"):
self.axioms_path = axioms_path
self.axioms_data = self._load_axioms()
logger.info(f"EthicalAxioms loaded from: {self.axioms_path}")
self._build_keywords_index()
def _load_axioms(self) -> Dict[str, Any]:
"""Loads ethical axioms from a YAML file."""
try:
with open(self.axioms_path, 'r', encoding='utf-8') as f:
return yaml.safe_load(f)
except FileNotFoundError:
logger.error(f"Ethical axioms file not found at {self.axioms_path}")
return {"version": "0.0", "description": "Default empty axioms."}
except Exception as e:
logger.error(f"Error loading ethical axioms from {self.axioms_path}: {e}")
return {"version": "0.0", "description": "Error loading axioms."}
def _build_keywords_index(self):
self.keywords_to_axioms = {}
for key, axiom in self.axioms_data.items():
if isinstance(axiom, dict) and 'keywords' in axiom:
for keyword in axiom['keywords']:
self.keywords_to_axioms.setdefault(keyword.lower(), []).append(key)
def get_axiom(self, axiom_id: str) -> Optional[Dict[str, Any]]:
"""Retrieves a specific axiom by its ID."""
return self.axioms_data.get(axiom_id)
def get_all_axioms(self) -> Dict[str, Any]:
"""Returns all loaded axioms."""
return self.axioms_data
def query_axioms_by_keyword(self, keyword: str) -> List[Dict[str, Any]]:
"""
Finds axioms related to a keyword.
Returns a list of axiom dicts.
"""
axiom_ids = self.keywords_to_axioms.get(keyword.lower(), [])
return [self.axioms_data[aid] for aid in axiom_ids if aid in self.axioms_data]
def check_principle_adherence(self, context: Dict[str, Any], principle_keywords: List[str]) -> Dict[str, Any]:
"""
Performs a conceptual check of adherence to principles based on keywords and context.
This is a placeholder for actual complex logical/simulation-based adherence checks.
Maps to a component of NeuralBlitz's Conscientia++.
"""
adherence_report = {
"is_adherent": True,
"principles_checked": [],
"message": "All checked principles appear to be adhered to.",
"violations": []
}
logger.debug(f"Checking adherence for keywords: {principle_keywords}")
for keyword in principle_keywords:
related_axioms = self.query_axioms_by_keyword(keyword)
for axiom in related_axioms:
axiom_name = axiom.get("name", "Unknown Principle")
adherence_report["principles_checked"].append(axiom_name)
# --- Dummy check logic for demo ---
# Example: If context indicates "disparity" and "fairness" is a keyword, mark as potential violation
if "disfair" in keyword or "equity" in keyword and context.get("has_disparity", False):
if "mitigated" not in context.get("action", ""): # Only flag if not mitigated
adherence_report["is_adherent"] = False
adherence_report["violations"].append(f"Potential violation of '{axiom_name}': Disparity detected and not explicitly mitigated.")
adherence_report["message"] = "Ethical concerns raised during principle adherence check."
elif "harm_detected" in context and context["harm_detected"]:
adherence_report["is_adherent"] = False
adherence_report["violations"].append(f"Potential violation of '{axiom_name}': Harm detected.")
adherence_report["message"] = "Ethical concerns raised during principle adherence check."
return adherence_reportThis module checks for consistency and ethical soundness.
import networkx as nx
import logging
from typing import Dict, Any, List
from src.ethical_reflection.ethical_axioms import EthicalAxioms
logger = logging.getLogger(__name__)
class CoherenceMonitor:
"""
Checks the logical and ethical coherence of causal graphs and policy proposals.
Maps to NeuralBlitz's VPCE (Veritas Phase-Coherence Equation) for structural integrity
and CECT for ethical bounds.
"""
def __init__(self, ethical_framework: EthicalAxioms):
self.ethical_framework = ethical_framework
logger.info("CoherenceMonitor initialized.")
def check_graph_coherence(self, causal_graph: Any, domain_axioms: List[str] = None) -> Dict[str, Any]:
"""
Performs structural and ethical coherence checks on a given causal graph.
Args:
causal_graph (Any): A NetworkX DiGraph or a CausalGraph wrapper object.
domain_axioms (List[str]): Keywords for specific domain axioms to check.
Returns:
Dict[str, Any]: Report on coherence, including violations.
"""
graph_nx = causal_graph.graph if hasattr(causal_graph, 'graph') else causal_graph
if not isinstance(graph_nx, nx.DiGraph):
raise TypeError("Input causal_graph must be a NetworkX DiGraph or CausalGraph object.")
report = {
"is_coherent": True,
"structural_valid": True,
"ethical_valid": True,
"message": "Causal graph is structurally and ethically coherent.",
"details": [],
"ethical_violations": []
}
# --- Structural Coherence Checks (VPCE Analog) ---
if not nx.is_directed_acyclic_graph(graph_nx):
report["structural_valid"] = False
report["is_coherent"] = False
report["message"] = "Graph contains cycles, which is structurally incoherent for a causal DAG."
report["details"].append({"type": "structural_error", "reason": "Graph cycle detected."})
# Check for isolated nodes that are not defined as exogenous
isolated_nodes = list(nx.isolates(graph_nx))
if isolated_nodes:
report["details"].append({"type": "warning", "reason": f"Isolated nodes found: {isolated_nodes}. Verify if these are truly exogenous variables or modeling omissions."})
# --- Ethical Coherence Checks (CECT Analog) ---
# Iterate through nodes and edges for potential ethical implications
for u, v, data in graph_nx.edges(data=True):
# Example: A causal link that implies harm or disparity (very simplified logic for demo)
edge_ethical_context = {
"source": u,
"target": v,
"relationship_type": data.get("causal_type", "direct_influence"),
"strength": data.get("strength", 0.0)
}
# Add conditions for checking specific axiom keywords.
# Example: if an edge goes to an 'outcome' node and has a negative effect,
# and is linked from a 'policy' node, it has ethical implications.
if ("outcome" in v and data.get("strength", 0.0) < -0.1 and "policy_change" in u) or \
("demog" in u and "demog" in v and u!=v and data.get("strength", 0.0) > 0.01): # e.g. one demog causes another demog outcome
logger.debug(f"Potential ethical implication for edge: {u} -> {v}. Running principle check.")
adherence = self.ethical_framework.check_principle_adherence(
context=edge_ethical_context,
principle_keywords=["harm_reduction", "fair_treatment", "non_maleficence"]
)
if not adherence["is_adherent"]:
report["ethical_valid"] = False
report["is_coherent"] = False
report["ethical_violations"].extend(adherence["violations"])
report["details"].append({"type": "ethical_concern", "source_edge": f"{u}->{v}", "violations": adherence["violations"]})
if not report["ethical_valid"]:
report["message"] = "Ethical inconsistencies or concerns detected in the causal graph."
elif not report["structural_valid"]:
report["message"] = "Structural coherence issues detected in the causal graph."
# Add specific checks based on domain axioms (e.g., if a fairness axiom expects no direct link from 'policy' to 'demographic_outcome_disparity')
if domain_axioms:
for axiom_keyword in domain_axioms:
related_axioms = self.ethical_framework.query_axioms_by_keyword(axiom_keyword)
for axiom in related_axioms:
if axiom_keyword == "fair_treatment_axiom":
# Specific graph pattern check for "fair_treatment_axiom"
# e.g., direct causal paths from "policy_change" to "demographic_outcome" must be mediated.
if "policy_change" in graph_nx.nodes():
for node in graph_nx.nodes():
if "demog" in node and graph_nx.has_edge("policy_change", node):
report["ethical_valid"] = False
report["is_coherent"] = False
violation = f"Violation of '{axiom_keyword}': Direct edge found from 'policy_change' to '{node}'. Should be mediated."
report["ethical_violations"].append(violation)
report["details"].append({"type": "ethical_pattern_violation", "violation": violation})
report["message"] = "Ethical concerns: Policy-to-demographic-outcome direct link violates fairness principle."
break
return report
def perform_deep_ethical_audit(self, causal_graph: Any) -> Dict[str, Any]:
"""
Performs a more comprehensive audit, including checking for specific anti-patterns
and simulating ethical counterfactuals (conceptual placeholder).
Maps to Conscientia++ deep audit capabilities.
"""
logger.info("Initiating deep ethical audit...")
basic_report = self.check_graph_coherence(causal_graph, domain_axioms=["fair_treatment_axiom"])
# --- Advanced Checks (Conceptual) ---
# Example: Simulating a counterfactual where a detected bias is 'removed' from the data
# and checking the causal graph for changes (requires causal_counterfactuals.py).
deep_audit_details = {
"basic_coherence": basic_report,
"anti_pattern_scan": "No critical anti-patterns detected.", # Placeholder
"simulated_ethical_counterfactual": {
"outcome_if_no_bias": "Conceptual (requires simulation)",
"confidence": 0.0 # Placeholder
}
}
# A simulated example of a deeper finding:
if basic_report.get("is_coherent", True):
if random.random() < 0.2: # Simulate finding a subtle bias
deep_audit_details["anti_pattern_scan"] = "Subtle unmeasured confounding inferred between 'education' and 'opportunity'."
basic_report["is_coherent"] = False # Update coherence status for overall report
basic_report["ethical_valid"] = False
basic_report["ethical_violations"].append("Deep audit: Subtle unmeasured confounding affecting 'fair_treatment_axiom'.")
basic_report["message"] = "Deep audit uncovered subtle ethical concerns."
logger.info("Deep ethical audit complete.")
return {"overall_coherence_report": basic_report, "deep_audit_details": deep_audit_details}This turns complex findings into human-readable text.
import logging
from typing import Dict, Any, Optional
import networkx as nx
logger = logging.getLogger(__name__)
class NarrativeGenerator:
"""
Generates human-readable narrative reports from causal graphs, ethical analyses,
and intervention proposals.
Maps to NeuralBlitz's LoN (Language of the Nexus) for coherence and context-rich outputs.
"""
def __init__(self, context: Dict[str, Any] = None):
self.context = context if context is not None else {}
logger.info("NarrativeGenerator initialized.")
def _generate_causal_narrative(self, causal_graph_data: Dict[str, Any]) -> str:
"""Generates a narrative description of the causal graph."""
graph_nx = causal_graph_data.graph if hasattr(causal_graph_data, 'graph') else None
if not graph_nx or not isinstance(graph_nx, nx.DiGraph):
return "No valid causal graph data to generate narrative from."
summary = causal_graph_data.get_causal_summary() if hasattr(causal_graph_data, 'get_causal_summary') else {"num_nodes": graph_nx.number_of_nodes(), "num_edges": graph_nx.number_of_edges(), "is_dag": nx.is_directed_acyclic_graph(graph_nx)}
narrative = f"### Causal Discovery Report\n\n"
narrative += f"A causal graph was inferred from the provided data using the {summary.get('inferred_algorithm', 'specified')} algorithm. "
narrative += f"The resulting graph contains {summary.get('num_nodes')} distinct variables (nodes) and {summary.get('num_edges')} identified causal relationships (edges). "
if not summary.get('is_dag', False):
narrative += "However, the graph contains cycles, indicating potential structural incoherence for a directed acyclic causal model."
else:
narrative += "The graph is a Directed Acyclic Graph (DAG), representing a valid causal structure."
# Describe some key relationships (simplified)
key_relationships = []
for u, v, data in graph_nx.edges(data=True):
if summary.get('num_edges',0) < 5: # Only if it's a small graph
key_relationships.append(f"'{u}' causally influences '{v}'.")
if key_relationships:
narrative += "\nKey relationships identified include:\n" + "\n".join([f"- {rel}" for rel in key_relationships])
return narrative
def _generate_ethical_narrative(self, coherence_report: Dict[str, Any]) -> str:
"""Generates a narrative description of the ethical coherence report."""
narrative = f"\n### Ethical Coherence & Bias Report\n\n"
overall_status = "coherent and adheres to our ethical framework" if coherence_report.get("is_coherent", True) else "contains significant ethical concerns or inconsistencies"
narrative += f"The analysis of the inferred causal graph found that the model {overall_status}. "
if not coherence_report.get("structural_valid", True):
narrative += "Structural errors, such as cycles, were detected, invalidating the core causal claims.\n"
if not coherence_report.get("ethical_valid", True):
narrative += "Ethical violations were detected during the assessment. Specifically:\n"
for violation in coherence_report.get("ethical_violations", []):
narrative += f"- {violation}\n"
elif not coherence_report.get("is_coherent", True) and not coherence_report.get("ethical_valid", True):
# This means an underlying deep audit found problems not caught by basic checks
narrative += f"{coherence_report.get('message', 'Subtle issues were detected during a deeper ethical audit.')}\n"
else:
narrative += coherence_report.get("message", "No specific ethical violations detected.") + "\n"
if coherence_report.get("anti_pattern_scan"):
narrative += f"\nAnti-pattern scan: {coherence_report.get('anti_pattern_scan')}\n"
return narrative
def _generate_intervention_narrative(self, policy_proposal_data: Dict[str, Any]) -> str:
"""Generates a narrative description of policy intervention proposals."""
narrative = "\n### Intervention Proposals\n\n"
if policy_proposal_data:
narrative += f"Based on the causal and ethical analysis, the following intervention is proposed to address identified disparities and promote flourishing:\n"
narrative += f"- **Proposed Action:** {policy_proposal_data.get('suggested_action', 'N/A')}\n"
narrative += f"- **Targeted Outcome:** {policy_proposal_data.get('target_outcome', 'N/A')}\n"
narrative += f"- **Predicted Impact (UFO Gain):** {policy_proposal_data.get('predicted_ufo_gain', 'N/A'):.2f}\n"
narrative += f"- **Primary Ethical Driver:** {policy_proposal_data.get('ethical_driver', 'N/A')}\n"
narrative += "\nDetailed impact simulations and trade-offs are available in the accompanying Decision Capsule.\n"
else:
narrative += "No specific policy interventions are proposed at this time.\n"
return narrative
def generate_narrative_report(self) -> str:
"""
Generates a full narrative report by combining insights from various contexts.
"""
report_parts = []
if "causal_graph" in self.context:
report_parts.append(self._generate_causal_narrative(self.context["causal_graph"]))
if "coherence_report" in self.context:
# Check if this is a deep audit report
if "overall_coherence_report" in self.context["coherence_report"]:
report_parts.append(self._generate_ethical_narrative(self.context["coherence_report"]["overall_coherence_report"]))
report_parts.append(f"\n#### Deep Audit Details:\n{self.context['coherence_report']['deep_audit_details'].get('anti_pattern_scan', 'No deeper issues reported.')}\n")
else:
report_parts.append(self._generate_ethical_narrative(self.context["coherence_report"]))
if "policy_proposal" in self.context:
report_parts.append(self._generate_intervention_narrative(self.context["policy_proposal"]))
final_report = "\n".join(report_parts)
logger.info("Narrative report generated.")
return final_report• GoldenDAG: f1e2d3c4b5a6f7e8d9c0b1a2d3e4c5b6a7f8d9c0b1a2d3e4c5b6a7f8 • Trace ID: T-v24.0-REFLEXIVE_ORACLE_CAUSAL_ETHICAL_GEN-a1b2c3d4e5f6a7b8c9d0e1f2a3b4c5d6 • Codex ID: C-REFLEXIVE_ORACLE-CAUSAL_ETHICAL_MODS-0009