The Sangedha Framework

Algorithmic Negligence and the Causal Forensic Framework for Culpability doc_id: AVT-FRM-2025-002 date: Q4 2025 classification: PUBLIC author: Alpha Vector Advanced Projects status: VALIDATED

Executive Summary

The Deployment Crisis: The deployment of autonomous AI systems in critical infrastructure has outpaced the legal profession's ability to assign liability when these systems fail catastrophically.

The Solution: This paper introduces a forensically sound, legally defensible framework for reconstructing mens rea (culpable mental state) in AI-driven security failures.

Strategic Criticality: An AI system is not an inscrutable oracle; it is the product of thousands of human decisions, each leaving an immutable digital trace. By organizing these traces into evidentiary domains and applying Causal AI, we can bridge the gap between technical failure and legal liability.

1. Introduction: The Accountability Gap in Autonomous Systems

1.1 Market Context and Scale

The rapid integration of autonomous AI into high-stakes domains has created a profound legal paradox. Organizations deploy systems capable of causing billion-dollar damages but often lack the forensic capability to explain why a specific failure occurred, leading to the "Black Box" defense.

1.2 The "AI Did It" Defense

The Liability Vacuum: * Defendant Claim: "Unforeseeable emergent behavior in a complex system."

Plaintiff Challenge: Proving negligence without visibility into the decision-making chain.
Result: Settlements without admission of guilt, leaving systemic risks unaddressed.

1.3 Legal Landscape Evolution

Strict Liability vs. Negligence: Courts are moving towards a negligence standard that asks, "Did the creators take reasonable care to prevent this specific outcome?"
The Foreseeability Test: If a failure mode was statistically probable and ignored, it is not "emergent"—it is negligent.

2. The Digital Proxy for Mens Rea: Four Domains of Evidentiary Artifacts

Core Thesis: Our framework organizes digital traces into four evidentiary domains that collectively reconstruct the "mental state" of the system's creators.

Domain 1: Training, Configuration, and Ontological Artifacts

Purpose: Document the foundational choices that define the AI's purpose and constraints.

Forensic Techniques: 1. Configuration Analysis: Mapping risk acceptance in

config.yaml

Chain-of-Custody Recorded

or environment variables.

Hyperparameter Forensics: determining if safety parameters were loosened for performance.

Domain 2: Version Control System (VCS) Histories

Purpose: Provide an immutable ledger of every code change, its author, timestamp, and justification.

The Git Forensic Standard: * SHA-1 Hash Chains: Tampering with any commit invalidates all subsequent hashes.

Signed Commits: GPG signatures prove author identity.
Reflog: Even rewritten history leaves traces.

Case Study: Semantic Provenance Analysis * Scenario: A security check was moved after the transaction execution.

Textual Diff: Minimal (line reordering).
Semantic Diff: Catastrophic (critical security bypass).
Conclusion: Demonstrable negligence in code review.

Domain 3: Operational and Inference Logs

Purpose: Provide the AI's decision transcript for forensic reconstruction.

Input vectors.
Confidence scores.
Activation patterns at the moment of failure.

Domain 4: Data Provenance and ETL Pipelines

Purpose: Document the origin of training data and transformations applied.

Bias Audit: Compliance with NYC Local Law 144 and EU AI Act.
Data Lineage: Tracking data from source to model weight influence.

3. The Causal Leap: From Correlation to Provable Causation

Traditional forensic analysis establishes correlation. The Sangedha Framework applies Causal AI to establish legal causation with statistical certainty.

3.1 The Legal Standard: But-For Causation

In tort law, liability requires proving: "The harm would not have occurred but for the defendant's negligent action."

Legal Standard: Preponderance of evidence (>50% probability).
Sangedha Target: >95% Confidence Interval via Structural Causal Models.

3.2 Causal AI: Technical Foundations

Methodology: 1. Judea Pearl's Causal Hierarchy: Moving from Association -> Intervention -> Counterfactuals.

Structural Causal Models (SCMs): Constructed from the four artifact domains.
DoWhy Framework: Microsoft Research library for causal inference.

Counterfactual Query: * "What would the probability of failure have been if the safety parameter had been set to

True

Chain-of-Custody Recorded

If the probability drops from 99% to 1%, causation is proved.

3.3 Case Study: TradeMind Trading Failure

Incident: Autonomous trading AI executed 47,000 unauthorized trades.
Defense: "Emergent behavior."
Sangedha Analysis: Causal graph revealed a recursive feedback loop introduced in a specific config change.
Result: Counterfactual analysis proved the crash would not have occurred without that specific change.

4. Implementation Framework

4.1 Organizational Readiness Assessment

AVT Forensic Maturity Model: * Level 1 (Ad Hoc): Basic logging, minimal defensibility.

Level 3 (Managed): Tamper-evident logs, VCS integration.
Level 5 (Causal Forensic): Full SCM, counterfactual analysis.

4.2 Technical Implementation Roadmap

Phase 1: Foundation (Months 1-3) * Logging: Tamper-evident storage (Amazon QLDB), RFC 3161 Timestamping.

VCS: Mandatory GPG-signed commits.

Phase 2: Integration (Months 4-6) * Data Provenance: Lineage tracking (DataHub).

Operational Logging: Confidence scores per inference.

Phase 3: Causal Analysis (Months 7-12) * Stack: DoWhy, CausalNex, TETRAD.

Phase 4: Operationalization (Months 13-18) * Automated Incident Response: Preservation -> Collection -> SCM Construction -> Analysis -> Reporting.

4.3 Governance Policies

AI System Development Standard: Mandates signed commits and logged inferences.
AI Incident Response Standard: Protocols for causal forensic investigation.

5. Legal and Regulatory Implications

5.1 Evolution of Fiduciary Duty

Caremark & Marchand: Boards have a duty to monitor mission-critical risks.
Sangedha Application: Implementing forensically sound AI monitoring is now a fiduciary duty.

5.2 Regulatory Framework Alignment

SEC Cyber Rules: Causal forensics provides required board visibility.
EU AI Act: Framework covers technical documentation and risk management requirements.

6. Advanced Topics

6.1 Adversarial Forensics

Threat: Log injection, timestomping, VCS rewriting.
Countermeasure: Blockchain-Anchored Forensics (Bitcoin OP_RETURN) for mathematical proof of time and integrity.

6.2 Privacy-Preserving Causal Forensics

Using Zero-Knowledge Proofs (zk-SNARKs) to prove causation without revealing sensitive underlying data (e.g., PII or PHI).

6.3 AI-Driven Forensics

Using an ensemble of AI models to cross-validate forensic findings and minimize investigator bias.

7. Conclusion

The accountability crisis in AI is not a problem of legal theory but one of forensic methodology. The Sangedha Framework provides the tools to pierce the "black box" and hold human actors accountable for their algorithmic agents.

Final Verdict: The organizations that embrace causal forensic frameworks today will be the ones that survive tomorrow's liability landscape.

Contact: gsangedha.desk@proton.me