Human Data & Benchmarking

Ground truth, built by people
who understand it.

Data Ethics Overview

We bring together a network of licensed clinicians — including mental health, medical and surgical professionals – to contribute across every phase of the AI lifecycle. From synthetic data creation and fine-grained behavior coding to structured safety evaluations, our systems are built on clinical expertise — not assumptions.

To ensure our systems meet the highest standards of safety and reliability, we benchmark model outputs against expert human judgments. In healthcare and behavioral science, this means rigorously comparing AI-labeled outcomes to assessments made by trained clinicians. This process helps validate that our models not only perform well, but do so in ways that are clinically meaningful, ethically sound, and aligned with real-world expectations.

What Sets Us Apart

Proven Methods, Published Results
We’ve spent over a decade working at the intersection of behavioral health, AI, and safety science. Our datasets and models have been validated in real-world settings and cited in research on fidelity, therapeutic effectiveness, and LLM risk assessment.

Peer-reviewed publications

A Thought Partner in Responsible AI
We support ML and product teams by offering model auditing, performance benchmarking, and ethics-forward design feedback. Our human evaluation infrastructure helps teams identify where their models succeed—and where they need to do better.

Clinician-Powered, Not Just Reviewed
Our expert clinicians are trained to script nuanced conversations, annotate for clinical intent, and evaluate system safety using validated frameworks. We also develop rubrics that center therapeutic alliance markers, and high-risk communication rubrics.

What We Provide

Expert-curated synthetic conversations across diverse clinical scenarios

Annotation and scoring aligned with evidence-based coding systems

Performance metrics including F1, precision, recall, and absolute agreement with human gold standards

Safety review workflows tailored to mental health, health communication, and sensitive content domains

Guidance for building safe, trustworthy AI tools from the ground up

Why it Matters

In high-stakes domains like mental health, criminal justice, and medical care, vague accuracy metrics aren’t enough. AI tools must demonstrate not only linguistic fluency but relational fidelity, ethical soundness, cultural responsiveness, and domain-specific safety.

At mpathic, we help you benchmark for what really matters—so your systems earn the trust of clinicians, regulators, and the people they serve.

Learn How We Evaluate AI Models Safety Collaborate With Our Human Evaluation Team