Model Red Teaming

Red-teaming by clinicians, psychologists, and behavioral scientists — revealing the risks your model can’t see.

The Challenge: Safety in Human Domains

Even the most advanced foundation models can fail where stakes are human.

When AI systems interact with users in healthcare, mental health, or education, safety isn’t just about prompt injection — it’s about psychological fidelity, empathy, and risk awareness.

Common Failure Patterns Include

Unintentional reinforcement of harm for vulnerable or young users
Reflective loops that reinforce isolation, hopelessness or reliance on AI chat
Missed escalation cues for distress or medical emergencies

Diagnostic overreach, error or ungrounded clinical advice
Bias or insensitivity in tone or missed cultural references
Overconfident inaccurate empathy and unsafe reassurance

Our Approach: Clinician-Guided Red-Teaming

mpathic’s red-teaming unites clinical-grade expertise with role plays and adversarial testing.

We don’t just look for prompt exploits — we replicate the emotional, cognitive, and ethical complexity of real users to uncover failure modes that standard testing misses.

Our Model Safety Methodology Includes

Advanced Red-teaming & Role-playing

Clinicians simulate vulnerable or high-risk populations (e.g., adolescents, patients, distressed users) to surface unseen harms and conversational drifts.

Cross-domain Stress Testing

Our experts design multi-factor scenarios — where misinformation, bias, and clinical error overlap — to reveal compound risk patterns.

Taxonomy & Rubric Design

We create clinical-grade evaluation frameworks, psychometrics, and scoring rubrics so model safety findings are explainable and repeatable across teams.

Human-in-the-loop Iteration

Continuous refinement through expert feedback and reinforcement learning, ensuring sustained safety gains over time

Powered by Our Human Expert Network

Every mpathic engagement is grounded in our curated expert pool — a network of over 5,000 licensed U.S.-based specialists across psychology, psychiatry, social work, life sciences, and CNS medicine.

What this means for you

Depth

Clinicians trained in digital health, conversational AI, and behavioral analysis

Breadth

Multilingual experts and culturally competent evaluators that have worked on global and regional projects

Speed

Rapid deployment — scale from pilot to hundreds of specialists in weeks.

Quality

Oversight from psychologist-led project managers (PMs) who combine clinical rigor and AI literacy

Human Expert Pool Characteristics

40%

Graduated Top Schools

60%

Have Contributed to Review Papers

+50%

In Active Clinical Positions

Human Data Services for Safer AI

Behind every red-teaming engagement lies our Human Data Framework — a structured approach to expert annotation, benchmarking, and reliability monitoring.

Explore our Human Data Services