AI Disclosure in Professional Settings: Judgment v . Reproducibility.
I just presented to an audience of lawyers in Ohio, and a hot topic in the legal community right now are court-mandated AI disclosure requirements. I think these issues, however, transcend the practice of law and are relevant to other professional settings.
As I was preparing to present (actually didn’t get to cover this directly), it dawned on me that we may be asking the wrong first question.
In my world, the debate is often framed as:
“Did you use AI?”
But I think the more important question is becoming:
“What exactly are professionals still responsible for after AI enters the workflow?” That should inform the scope of the required disclosure.
Across professions, an ethical norm is clearly emerging around transparency in AI-assisted work. But there is growing disagreement about what that transparency should actually require.
Right now, the landscape seems to be dividing into three broad models:
No disclosure requirement, but a clear and strict reminder of mandatory verification obligations.
General disclosure of AI use combined with verification obligations. “I certify that I used AI and verified it.”
Mandatory disclosure of the specific AI tool and workflow used plus verification obligations. This is the most onerous of all.
What fascinates me is that the “correct” answer may depend heavily on the profession itself.
In science, reproducibility is foundational. I use expert witnesses all the time. In fact, last week I was at a Daubert hearing that challenged the scientific reliability of my forensic pathology expert. If my pathologist uses AI to perform a forensic analysis without documenting the tools used or the specific workflow and methodology, then her opinion is likely not scientifically reliable enough to be admissible evidence in court. In that world, the AI model, prompts, datasets, parameters, and workflow may effectively become part of the methodology itself.
But many professions do not operate this way.
Lawyers, writers, strategists, consultants, and negotiators are often judged less by reproducibility and more by:
• accuracy,
• competency,
• professional judgment,
• supervision,
• and outcome reliability.
A legal argument is not expected to be scientifically reproducible. Two lawyers can ethically arrive at the same conclusion through entirely different cognitive processes, workflows, technologies, and experiences.
That distinction may become one of the defining fault lines in AI ethics:
Fields grounded in reproducibility may increasingly demand detailed AI methodology disclosure.
Fields grounded in human judgment may ultimately care far more about verification, accountability, and supervision than the identity of the tool itself.
This is one reason I have increasingly focused my presentations for lawyers on what I call “The Verification Ritual” rather than blanket slogans like “just verify.”
My framework is TMD:
Tangible — Print it out. Physically touch a document.
Methodical — Check every case, quote, fact, and statute against the original source material.
Documented — Highlight every verified source and slow down the process by physically writing and highlighting your verification. Create evidence of your own verification process.
The consequences of moving too fast in an AI-enhanced world are simply too great for lawyers where their professional licenses—not to mention reputations— are on the line.
For me, the only mandatory disclosure requirement for lawyers should be to the client, because the client should have the opportunity to make an informed decision about the confidentiality of their information and whether they consent to the use of AI tools that could potentially expose or process sensitive private data.
Beyond that, I believe the profession should focus less on whether AI was used all and more on whether the lawyer exercised competent supervision, sound judgment, and rigorous human verification.