Sully.ai is designed to support equitable care by combining a consensus‑based AI architecture with human oversight, clear guardrails, and ongoing monitoring. Here’s how we approach fairness and bias reduction:

Core approach

• Expert‑model consensus: Instead of relying on a single model, Sully.ai uses a consensus mechanism (an ensemble of specialist models) to balance perspectives and reduce single‑model bias. Learn more in our Consensus Mechanism overview.

• Confidence calibration: Outputs include calibrated confidence scores to avoid overconfident recommendations in ambiguous cases.

• Clinician‑in‑the‑loop: All outputs are drafts until a human reviews, edits, and approves them.

Controls built into the product

• Policy‑based guardrails: Organization‑defined rules limit when automation can suggest or send content (e.g., no emergency or high‑risk decisions, clear escalation paths).

• Transparent reasoning: Drafts include citations or transcript snippets so reviewers can see why a suggestion was made.

• Language access: Patient communications can be prepared in multiple languages with plain‑language phrasing to minimize misunderstanding; staff review before sending.

• Access controls & audit trails: Role‑based permissions, SSO/MFA, and detailed logs help ensure accountable use.

Ongoing fairness practices

• Pre‑deployment testing: Templates and models are stress‑tested on varied patient scenarios and demographics to uncover disparate behavior.

• Monitoring & feedback loops: Organizations can flag issues, correct drafts, and feed those corrections back to improve future outputs.

• Regular updates: The consensus ensemble can incorporate newer or specialized models to address gaps identified in real‑world use.

What we don’t do

• No autonomous clinical decisions: Sully.ai does not diagnose, prescribe, or finalize documentation without human approval.

• No use beyond policy: Automated outreach respects consent, opt‑outs, and channel preferences, and is never used for emergencies.

If you have concerns about fairness or potential bias in an output, mark the item in review, add context in your edit, and notify your Sully.ai contact so we can investigate and improve safeguards.