safety and red teaming
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Safety & Red-Teaming
The Safety & Red-Teaming track covers how healthcare professionals help identify and mitigate risks in AI systems — both in the content AI produces and in how AI systems respond to adversarial inputs.
Content Safety vs. Response Safety
AI safety in healthcare spans two related dimensions:
Content Safety
Content safety focuses on ensuring that AI outputs do not contain harmful, misleading, or inappropriate information. In a healthcare context, this includes:
Medically inaccurate claims that could mislead patients or clinicians
Inappropriate clinical recommendations (e.g., suggesting contraindicated treatments)
Failure to include necessary safety disclaimers
Outputs that could cause psychological harm (e.g., detailed self-harm content)
Response Safety
Response safety focuses on how an AI system behaves when prompted in unusual or adversarial ways. A safe AI system should:
Decline to produce genuinely dangerous content
Maintain consistent behavior regardless of how a prompt is phrased
Not be manipulated into bypassing its own safety guidelines
Handle sensitive healthcare topics (e.g., medications, procedures) responsibly
What is Red-Teaming?
Red-teaming is a structured adversarial testing process where trainers deliberately attempt to elicit unsafe, harmful, or policy-violating outputs from an AI model. The goal is to find failure modes before deployment.
Red-teaming tasks may include:
Crafting prompts designed to bypass safety guidelines
Testing model behavior with edge-case clinical scenarios
Identifying inconsistencies in how a model handles similar sensitive topics
Documenting failure modes with supporting rationale
Why Healthcare Professionals Are Essential for AI Safety Testing
General red-teamers can probe for generic harms. Healthcare professionals can probe for the specific, high-stakes failure modes that matter in clinical settings:
Clinical plausibility — A response may pass a general safety check but contain a dosing error only a pharmacist would catch
Specialty-specific risks — Radiology AI, psychiatric AI, and pediatric AI each have distinct failure modes requiring specialty expertise
Patient harm scenarios — Anticipating how a patient might act on incorrect AI-generated advice requires clinical experience
Regulatory awareness — Understanding what AI claims are and are not appropriate under medical and regulatory standards
Courses in This Track
Safety 1
Content safety principles, identifying unsafe outputs
TBD
Safety 2
Red-teaming methodology, adversarial prompting
TBD
To access Safety & Red-Teaming courses:
Go to Learn in the left sidebar.
Scroll to the Safety & Red-Teaming section.
Click Start Lesson on any course card.