Expert Witness Services
The Question
Organizations deploy AI systems that make consequential decisions. The question courts increasingly face is whether they had adequate grounds to trust those systems before doing so.
I offer expert analysis on AI governance adequacy, system reliability, and epistemic risk: the question of whether an organization had sufficient processes to understand what its AI system was doing, and what failure modes were foreseeable, before deployment.
This is distinct from engineering testimony about how a system was built. It concerns the governance and evaluation standard: what testing was done, what it could and could not detect, and whether the organization's oversight processes met the level of rigor the deployment context required.
The Distinction
Most AI litigation focuses on what a system did. The prior question — whether the organization had adequate grounds to trust it before deployment — is where governance adequacy testimony applies.
Standard audits confirm internal consistency. They do not confirm that a system's outputs correspond to operational reality. When that gap becomes visible in a consequential context, the question of what the deploying organization knew, and what they were capable of knowing, is the evidentiary question.
Areas of Testimony
Adequacy of AI governance and pre-deployment evaluation
AI system reliability and foreseeable failure modes
Automated decision-making and epistemic risk
Algorithmic accountability and oversight standards
AI risk management frameworks (NIST AI RMF, ISO 42001)
Organizational AI governance program assessment
Engagement Types
Available for retention across the litigation lifecycle.
Early assessment of AI governance adequacy, identification of evidentiary questions, and analysis of whether organizational oversight processes meet applicable standards.
Written analysis of AI system governance, reliability assessment, and epistemic risk evaluation for use in federal and state civil litigation and regulatory proceedings.
Expert testimony on AI governance standards, oversight adequacy, and the epistemic gap between what organizations can verify and what they claim to know about their systems.
Analysis and testimony in administrative and regulatory contexts involving AI system deployment, compliance failures, and governance adequacy.
Background
I am a director-level research administrator at Harvard University with over 20 years in research operations, regulatory science, and compliance — fields defined by the question of whether a process is rigorous enough to justify confidence in its outputs.
I am the founder of VeracIQ LLC, which holds a patent-pending mechanism for detecting epistemic drift in deployed AI systems. I am a Founding Partner and Head of Epistemic Integrity at the Institutional Coherence Initiative, and an advisory board member of the Institute for Tax AI Governance.
I am a candidate for the IAPP AI Governance Professional (AIGP) certification. I have been invited to speak on AI governance at the NYC Bar Association and the Duke-NUS Medical School digital health workshop, and have participated in panels for practitioners and policy makers.
Publications & Commentary
Retain or Inquire
Available for consultation on matters involving AI governance adequacy, automated decision systems, and AI reliability assessment. Engagements considered in federal and state civil litigation, regulatory proceedings, and pre-litigation consulting.
Geographic availability: nationwide. Remote testimony available. VeracIQ is patent pending. Provisional No. 63/858,627.