Deception TRACER

Deception TRACER Responsible AI Framework

1. Mission

1. Mission
Deception TRACER applies advanced artificial-intelligence models to assist investigators in detecting linguistic indicators of deception.
Its purpose is to enhance human decision-making — never to replace it.
Every component of this framework is built on three pillars: Accountability, Transparency, and Fairness.

  1. Origin and Pedigree

    Deception TRACER’s analytical engine is built on a large-language-model foundation provided by OpenAI (GPT-5) and further refined through proprietary fine-tuning by Deception TRACER’s team of U.S. Counterintelligence and Interrogation professionals.

    Each model version is documented with:

    • Source model identifier and release date
    • Fine-tuning data descriptions and validation sets
    • Change-control log and version hash
  2. Chain of Custody

    All model training, evaluation, and deployment events are recorded in a secure audit trail.

    Each deployed version is cryptographically signed and reproducible, ensuring integrity, traceability, and evidentiary reliability.

  3. Transparency Commitment

    While specific training data cannot be released due to privacy and licensing restrictions, Deception TRACER maintains internal provenance documentation detailing:

    • Data collection methods
    • Anonymization and consent procedures
    • Bias and fairness validation reports

    Authorized oversight entities may review these materials under NDA or statutory authority.

    1. Role of the HumanDeception TRACER is an analytical tool, not a truth-adjudication engine. Every output must be reviewed and interpreted by a qualified investigator or supervisor. No disciplinary or legal action may rely solely on AI-generated findings.
    2. Investigator Responsibilities
      • Contextualize: Interpret AI flags within the full statement and investigative context.
      • Challenge: Document any disagreement with AI indicators.
      • Clarify: Use highlighted language to refine questioning, not to accuse.
      • Corroborate: Validate AI insights against physical evidence, behavior, and corroborating testimony.
    3. Supervisory OversightSupervisors review usage logs to verify adherence to HITL policy. All outputs are stored with timestamps, model version numbers, and user identifiers for chain-of-custody auditing.
    4. Transparency NoticeEvery report automatically includes:
      “This analysis was generated by Deception TRACER’s AI model and reviewed by a human investigator. The model identifies linguistic indicators of possible deception but does not determine truth or falsity.”
    5. Continuous Improvement LoopHuman feedback is systematically analyzed to:
      • Reduce false positives
      • Improve interpretability
      • Identify emerging linguistic patterns in deception
      No personally identifiable information is used in model retraining without explicit consent or authorization.
  1. Objective

    Ensure that Deception TRACER performs equitably across demographics, dialects, and operational contexts — minimizing false flags or omissions due to linguistic diversity.

  2. Evaluation Domains
    1. Demographic Bias: Detecting uneven performance across gender, race, or age groups.
    2. Linguistic Bias: Assessing fairness across dialects, regionalisms, and second-language usage.
    3. Operational Bias: Monitoring variation caused by differing interview styles or transcription quality.
  3. Methods
    • Pre-Deployment Testing:
      • Benchmark across anonymized, diverse datasets.
      • Quantify false-positive rates by subgroup; maintain variance ≤ 3%.
      • Apply adversarial linguistic testing to stress-check stability.
    • Post-Deployment Monitoring:
      • Aggregate statistics continuously logged (no PII).
      • Periodic human review for drift detection.
    • Independent Audits:
      • External experts periodically review anonymized outputs for fairness and proportionality.
  4. Mitigation Protocols
    • Adjust confidence thresholds when subgroup variance exceeds tolerance.
    • Exclude biased samples from retraining.
    • Insert transparency disclaimers:
    “Indicators reflect language patterns, not demographic attributes.”
    • Require human arbitration when potential bias is suspected.
  5. Documentation

    Each model update is accompanied by a Bias Evaluation Report that records:

    • Datasets and metrics used
    • Findings and remedial actions
    • Reviewer sign-offs and retention schedule

Deception TRACER aligns with CJIS, DOJ, and agency-specific standards governing the ethical use of AI in investigations.
All data are encrypted in transit and at rest, processed under least-privilege access control, and retained according to law-enforcement recordkeeping statutes.
Use of the system constitutes acceptance of these policies.

Deception TRACER delivers intelligence — not judgment.
Its insights empower trained professionals to detect deception more efficiently, while maintaining fairness, accountability, and lawful integrity.