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Our Commitment to Responsible AI

We build healthcare AI that’s safe, compliant, and transparent – certified through our partnership with Pacific AI.

John Snow Labs’ AI Governance Framework conforms to 200+ laws,
regulations, and industry standards worldwide.

Governance

We operate a full lifecycle AI governance program across nine pillars:
Risk Management

threat modeling, controls, and continuous evaluation

Safety

robustness, red-teaming, and clinical guardrails

Copyright

IP-safe training and use; protected content handling

System Lifecycle

gated advancement from design → deployment → retirement

Privacy

data-minimization, access control, and de-identification

Incident Reporting

defined triggers, SLAs, RCAs, and customer notice

Transparency

model cards, benchmarks, and decision traces

Fairness

pre-deployment bias testing; ongoing monitoring

Acceptable Use

permitted/prohibited use cases; abuse prevention

Transparency

We publish peer-reviewed work – benchmarks, metrics, system designs, and head-to-head comparisons – so buyers can independently verify claims
  • Clinical de-identification benchmark: 96% PHI F1 for JSL Medical LLMs vs Azure 91%, AWS 83%, GPT-4o 79%; with cost model details for local deployments.
  • TrustNLP workshop: methodology for measuring Robustness, Accuracy, Toxicity with leaderboards for independent review.
  • Assertion detection (ECIR 2025): model architectures and results compared to GPT-4o and other major APIs.
  • Pharmacoepidemiology study (FDA Sentinel/MOSAIC-NLP): end-to-end pipeline, metrics, and real-world outcomes.

Privacy

Private by design: your data never leaves your environment

Customer-hosted models: JSL LLMs and SLMs deploy on-premises, in private cloud, or in air-gapped settings. No external “model-as-a-service” is required.

Zero data sharing: Products like Generative AI Lab support fully isolated deployments, with audit trails and RBAC.

Data residency & sovereignty: Local deployments help meet residency rules and sector-specific requirements.

Privacy-preserving pipelines: Reference architectures for DICOM and metadata de-identification with OCR, PHI detection, obfuscation, and full audit logging.

Fairness

We use and contribute to LangTest (open source) to run comprehensive bias & fairness testing prior to deployment and as part of CI/CD
  • Breadth of testing: 100+ test types spanning fairness, bias, robustness, toxicity, representation, and accuracy; compatible with major NLP/LLM frameworks.
  • Metrics out of the box: gender-aware F1 & ROUGE thresholds; fairness checks for QA and summarization; automatic pass/fail gating.
  • Bias stress-tests: stereotype evaluation (e.g., gender/occupation), configurable categories (e.g., ethnicity, religion, nationality), and custom datasets.
  • Benchmarks & leaderboards: reproducible results and historical tracking to detect regressions over time.

Let’s Talk

Explore how our Pacific AI–certified governance, transparent benchmarking, privacy-first deployments, and fairness testing can accelerate your AI roadmap.

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