Governance & Risk

Bias, Hallucination, and Transparency

Models learn from historical data, so they reproduce historical bias — in hiring, lending, or triage, that becomes a legal and ethical exposure, not just a technical flaw. Hallucination is the model stating something false with complete fluency and confidence; it is inherent to how these systems work and is mitigated, never eliminated, primarily by grounding answers in retrieved sources with citations. Transparency means users always know they're interacting with AI and can see what an answer was based on.

• Ground answers in retrieved sources and show citations
• Test outputs across demographic groups before deployment
• Disclose AI involvement to users, always

Privacy and Intellectual Property

Anything you paste into a public AI tool may leave your control. Client records, patient information, contracts, and proprietary methods require a vendor agreement that explicitly forbids training on your data — enterprise tiers generally provide this and free tiers generally do not. On the output side, ownership and copyright of AI-generated material remain unsettled in many jurisdictions, so treat generated content as a draft you review and adopt rather than a clean asset you own outright.

Regulatory Compliance

Rules are tightening and vary by jurisdiction and sector. The EU AI Act imposes obligations scaled to risk level. HIPAA governs health information, and Massachusetts 201 CMR 17.00 requires a written security program and encryption of personal information — which unencrypted use of public AI tools straightforwardly violates. The durable approach is to build for the strictest regime you plausibly face: written policy, data minimization, encryption, vendor vetting, and an audit trail of what the system did and who approved it.