Built on the Conviction That AI Accountability Is a Shared Responsibility
Integrity Distributed was founded to address a structural gap: the tools, standards, and frameworks needed to verify AI behavior at scale do not yet exist in the open. We are building them.
The quality control of AI systems has, until now, been largely proprietary — conducted behind closed doors by the same organizations that build and deploy the systems being evaluated. This creates a fundamental conflict of interest that no amount of internal process can fully resolve.
Integrity Distributed was established to change that. We operate as an independent, non-profit research organization with no commercial stake in any AI system we evaluate. Our work is funded by foundations, public institutions, and individual donors who share our conviction that AI governance must be open, rigorous, and democratically accountable.
We convene researchers, regulators, engineers, and civil society organizations to develop the shared infrastructure of AI quality control: benchmarks, audit frameworks, compliance architectures, and governance standards that any organization can adopt, scrutinize, and improve.
How We Work
Independence
We accept no funding from organizations whose AI systems we evaluate. Our assessments are structurally insulated from commercial influence.
Openness
Our methodologies, benchmarks, and findings are published openly. Scrutiny is not a threat to our work — it is the mechanism by which our work improves.
Rigor
We apply the standards of peer-reviewed research to governance work. Assertions without evidence are not findings; frameworks without validation are not standards.
Equity
The costs and benefits of AI systems are not distributed equally. Our work is oriented toward those who bear disproportionate risk from AI failure.
Leadership & Advisors
Integrity Distributed is led by practitioners with deep experience across AI research, regulatory policy, and standards development.
Executive Director
Dr. Amara Osei
Former AI policy lead at a major standards body. Research background in algorithmic auditing and fairness metrics.
Director of Research
Dr. Lena Hartmann
PhD in machine learning with a focus on robustness evaluation. Previously led red-teaming programs at a national AI safety institute.
Director of Policy & Compliance
Marcus Delacroix
Regulatory counsel with experience across EU AI Act implementation, NIST frameworks, and cross-jurisdictional compliance architecture.
Director of Privacy & Distributed Systems
Dr. Priya Nair
Researcher in federated learning and differential privacy. Contributor to ISO/IEC standards on privacy-preserving AI.