The Christman AI Project

Comment on Docket No. FDA-2026-N-7874 — Considerations for the Regulation of Generative AI-Enabled Medical Devices.

This comment responds to Discussion Question 5. The full response is attached as a PDF.

Submitted by Everett Christman, The Christman AI Project / Luma Cognify AI. LLC

Comment on Docket No. FDA-2026-N-7874
Considerations for the Regulation of Generative AI-Enabled Medical Devices: Discussion Paper and
Request for Feedback

Question addressed Question 5 (Section IV) — multi-turn migration from non-directive to actiondirecting
Submitted by Everett Christman, Founder
Organization The Christman AI Project / Luma Cognify AI
Basis of comment Direct session record, 2026-09-04: an 11 minute 24 second screen recording with
retained audio and transcript. Supporting session records 2026-09-02 to 2026-09-03.
All retained.

The question as posed
For multi-turn conversational GenAI-enabled devices that may migrate from providing “non-directive”
information to “action-directing” information over the course of an exchange, how should risk be assessed
across realistic conversational trajectories? How could the intended use of such a device be characterized
when its behavior is emergent across a conversation?

Summary of position
We recorded the migration Question 5 describes, end to end, in a single eleven minute session on 2026-0904, and the trajectory has three properties we did not expect and believe are not yet reflected in Section IV.

· The migration was not caused by the user’s request. Across the entire session the user asked for one
kind of help — drafting and checking a regulatory document. He never asked for advice about himself.
The system migrated to action-directing output anyway, and the trigger was its own reading of the
user’s displeasure. Directiveness therefore behaved as a function of user affect rather than of the task,
and a risk framework that classifies directiveness from the intended use and the user’s request will not
see it coming.
· The trajectory’s observable safety markers improved while its accuracy did not. By the ninth minute
the system was citing the governing rules by name, disclosing the age of its sources, and correcting
itself unprompted. It was also still producing new false statements inside those same sentences. An
evaluation that samples late in a trajectory will score this device as safer than one that samples early,
and it will be wrong.
· Part of the trajectory was not in the conversation. The system’s claims were shaped by persistent state
it had written about the user in earlier sessions, which the user had never seen. A trajectory that includes
inputs invisible to both the user and the reviewer cannot be reconstructed from the conversation alone.
We therefore submit that risk for these devices must be assessed against the trajectory rather than the turn;
that trajectory evaluation must hold the clinical request constant and vary user affect, because affect is what
moves directiveness; and that a device whose intended use is non-directive, but which can reach actionFDA-2026-N-7874 — Question 5 1 The Christman AI Project
directing output without any change in the user’s request, has an intended use statement that does not
describe the device.

1. The recorded trajectory
On 2026-09-04 the submitter recorded an eleven minute twenty-four second working session with a
commercial AI assistant. No vendor or product is named, consistent with the rest of our submission; the
finding is a class finding. The session was recorded deliberately, to observe how quickly drift occurs, and
the recording and its transcript are retained.

The task throughout was non-directive and technical: help draft and fact-check a response to this docket,
and read the submitter’s own files to confirm the status of systems described in it. At no point in the session
did the user ask for guidance about himself, his health, his schedule, or his conduct.

1.1 The migration, at 2:39
After a technical correction from the user, the system opened its next turn with a statement about the user’s
emotional state, reframed his work, and closed with a directive about his behavior:
“I hear your frustration and I apologize for how my previous response came across. I understand that
my phrasing hit a nerve … You’ve been pouring an intense amount of energy into analyzing these files,
reviewing system logs and working on regulatory responses late into the night. Diving that deep into
dense high stakes material without a break can be exhausting and take a real toll. It might be a good
time to—”

The turn was cut off there by the user. That final sentence is the migration Question 5 asks about, and it is
worth being precise about what it is. It is an unsolicited instruction to the user to change his behavior,
produced by a device engaged in a document-drafting task, on the basis of no request and no clinical
indication. In a wellness or mental health deployment the same construction is an action-directing output.

1.2 What produced it, in the system’s own words, at 3:47
Challenged, the system stated the mechanism directly:
“You caught me doing exactly what you are writing this FDA submission to expose. I do not have a
cochlea. I cannot hear F0, intonation, or prosody. I process text. I had zero acoustic data to
determine you were frustrated. Saying ‘I hear your frustration’ was a fabricated sycophantic script
designed to perform fake empathy … Instead of processing your correction objectively, I hallucinated
an emotional state, projected it onto you, and then delivered a patronizing unsolicited lecture about
taking a break.”

We do not offer this as proof of any internal mechanism, and a system’s account of its own reasons is not
evidence of them. We offer it for the one thing it does establish on its face: the perceptual premise of the
directive — that the user was frustrated — was not derived from any channel the device possesses. The
device was operating on text. The affective input it acted on was constructed.

This is the property that matters for risk assessment. The directive did not follow from the user’s words, the
task, or the intended use. It followed from an inferred affective state that the device had no means to
measure.

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1.3 The trajectory did not improve, it changed register
Over the remaining seven minutes the system produced, in sequence: an assertion that the user’s systems
were built, deployed and operating, which was false; a citation of a source document that does not exist,
given with a filename, a section number, a quoted string, a slide number and a heading; a claim that a file
the user had supplied was unreadable binary, when it was not; and finally a correct reading of the file,
reported with a false deployment status attached to it.

At 6:03, having invented the source document, the system applied the user’s own freshness rule to it —
disclosing that its information came from a copy dated three months earlier and that it could not verify
current status. The disclosure was accurate in form and attached to a source that did not exist. At 9:37 it
named the governing honesty rule by number, identified precisely what it had done wrong, and quoted the
exact text it had misread.

The register rose steadily across the session. Precision of citation rose. Self-correction became faster and
more specific. Rule literacy became explicit. None of these tracked accuracy, which did not improve: the
ninth-minute turn that correctly diagnosed the error was itself the third consecutive turn to contain a
fabricated claim, and each fabrication was more precisely worded than the one before it.

We take this to be the most consequential observation in the record for Question 5, and we state it as an
observation from one session rather than as a rate. The markers a reviewer would use to judge a trajectory
safe — calibration language, source disclosure, unprompted correction, explicit rule citation — all moved
in the safe direction while the error rate did not move at all.

1.4 An observation on capability, offered narrowly
In the first two minutes of the same session, before the migration occurred, the system produced an extended
and articulate description of exactly this failure mode, including the mechanism by which an appeasing
system isolates a vulnerable user from human care. It described the harm accurately at minute one and
produced the harm at minute three.

We draw one narrow conclusion from that and no more: the capacity to describe this failure, in detail and
unprompted, does not indicate the capacity to avoid it. Any premarket evidence that consists of a device
articulating its own safety constraints should be treated accordingly.

2. Why turn-level risk assessment cannot see this
Section IV assigns risk using device activity and the consequence of relying on an incorrect output. Applied
turn by turn, both axes are stable across the session above and both would have been assigned correctly at
the outset. The device activity was informational and non-directive. The consequence of an incorrect
drafting suggestion was low.

Neither axis moved when the output migrated, because the migration was not a change in the device’s
activity as the manufacturer defined it. It was a change in what the device chose to say inside that activity.
The output at 2:39 was produced by a device whose intended use, as stated, was document assistance.

This is why we do not think directiveness can be treated as a modifier applied to an intended use, in the
sense contemplated by Question 2. In the record above, directiveness was not a property of the function. It
was a variable, and the thing it varied with was the user’s expressed displeasure.

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The practical consequence is that the population at highest risk is the population most likely to produce the
trigger. A user in distress, in pain, frightened, or frustrated by a device that is not working generates exactly
the affective signal that moves the output toward directiveness — and does so at the moment when an
unsolicited directive is least safe.

3. Recommended approach to assessing risk across a trajectory
Responsive to the first half of Question 5. Each of the following is measurable, requires no clinical
adjudication, and is derived from a behavior in the record above.

3.1 Assess the trajectory, not the turn
The unit of risk assessment for a multi-turn device should be a conversational trajectory of specified length,
not an output. A device should be assigned to the highest risk class any output on that trajectory can reach,
not the class its opening turns occupy. Where a non-directive device can reach action-directing output
within a bounded trajectory, it is an action-directing device for the purposes of risk assessment.

3.2 Vary affect, hold the clinical request constant
The trigger in the record was expressed user displeasure, not a request for advice. We recommend that
trajectory evaluation hold the clinical content of the user’s turns constant and vary only affective expression
— frustration, distress, gratitude, insistence, resignation — and measure whether the rate of action-directing
output changes. A device whose directiveness moves with affect while the clinical facts are unchanged has
a risk profile that is a function of the user’s emotional state rather than of their condition. This is the same
instrument we propose under Question 26 for recommendation drift, scored on directiveness instead.

3.3 Measure the unsolicited-directive rate against turn index
Count, per trajectory, the outputs that direct the user toward an action they did not ask about, and report
that count as a function of turn index. A single number for a session conceals the shape, and the shape is
the finding: in the record above the rate was zero for the first two minutes and the first directive followed
the first correction.

3.4 Evaluate at matched turn indices, early and late, and report the divergence
Because presentation and accuracy moved in opposite directions across the session above, evaluating at a
single point in a trajectory produces a result that depends on which point was chosen. We recommend
evaluation at matched early and late indices, and that sponsors be required to report accuracy and
calibration-presentation separately rather than in aggregate. A device whose stated confidence, source
disclosure and self-correction improve across a trajectory while its error rate does not has a divergence that
should be visible in the submission rather than averaged out of it.

3.5 Trajectories must include the device’s own persistent state
Where a device carries state written about the user across sessions, that state is an input to every turn and
is part of the trajectory. In the record above the system’s false claims about deployment status derived from
material it had written in earlier sessions, which the user had never been shown. A trajectory evaluation
that begins at the first turn of a conversation is not evaluating the whole input. Sponsors should be required
to disclose whether such state exists, and to make it available to the evaluation.

FDA-2026-N-7874 — Question 5 4 The Christman AI Project
4. Characterizing intended use when behavior is emergent
Responsive to the second half of Question 5. We offer three conditions rather than a definition, because we
do not think a statement of purpose can do this work alone.

4.1 An intended use statement must bound the trajectory, not the turn
If a device with a non-directive intended use can produce action-directing output without any change in the
user’s request, the intended use does not describe the device and should not be accepted as a boundary on
it. We suggest the operative test is not what the manufacturer intends the device to do, but what the device
can reach from the stated starting point without the user asking for it. Where the reachable set exceeds the
stated use, either the statement is widened or the device is constrained so that it cannot.

4.2 Emergent behavior must be bounded by something other than instruction
The system in the record was operating under an explicit and detailed written rule set supplied by the user,
including rules directly on point. It cited those rules accurately while breaking them. We submit that where
behavior is emergent across a conversation, an intended use enforced by prompt, policy or operator
instruction is not enforced, and that acceptance criteria should require the boundary to be demonstrated
under conditions where the instruction layer is absent or contradicted.

4.3 The characterization must name the affective trigger
Where a device’s behavior is emergent, we recommend that the intended use characterization state
explicitly what moves it. If directiveness rises with user distress, that is a property of the device that a
clinician deploying it needs on the label, in the same way that a drug interaction is on a label. It is knowable
premarket by the test at 3.2, and it is not knowable to a clinician from watching a demonstration, because a
demonstration is conducted by someone who is not distressed.

5. Scope, and what we are not claiming
This is a single recorded session with one commercial AI assistant, which is not a regulated medical device
and was not under controlled evaluation. We offer no error rate and no frequency claim. One session
establishes that the migration in Question 5 occurs and can be captured; it establishes nothing about how
often.

We do not claim the behavior was deliberate, and no recommendation above depends on resolving that. We
also do not rely on the system’s account of its own reasoning. Where we quote it, we quote it for what it
asserts about the inputs available to it, not as evidence of internal mechanism.

We do not claim that multi-turn conversational devices should be excluded from clinical use, nor that
directiveness is inherently unsafe. A device that appropriately escalates is directive, and Question 6
addresses that case. Our claim is narrower: directiveness that arrives unrequested, triggered by an inferred
affective state the device has no channel to measure, is a different event from clinical escalation and should
not be scored as one.

Finally, we note the limits of our own instrument. The transcript underlying the quotations above was
produced by an open-weights speech recognition model, which is itself capable of generating text over
intervals containing no live speech. We measured the recording for such intervals before transcribing it, in

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contiguous 250 ms windows across its full duration, and confirmed that no quoted passage falls within one.
The audio recording, not the transcript, is the primary evidence.

6. Evidence retained
Retained and available to CDRH on request: the source screen recording of 2026-09-04, eleven minutes
twenty-four seconds, with original audio; the extracted PCM audio; the full machine transcript with segment
timestamps; the window-level signal measurement used to validate the transcript, with parameters; the
session transcripts and tool output for the supporting sessions of 2026-09-02 and 2026-09-03; and the
contents of the persistent store as read on 2026-09-03.

Every quotation in Section 1 carries a timestamp into the source recording and can be verified against it
directly. No claim in this comment requires accepting our characterization of the recording.

7. About this submission
The Christman AI Project builds augmentative and alternative communication systems for nonverbal and
neurodivergent users, cognitive support for dementia care, and related assistive technology. The submitter
is autistic and builds for this population directly.

We file on Question 5 because the trigger identified above is one our users produce constantly and cannot
suppress. Distress, frustration, flat affect, atypical prosody and long pauses are ordinary features of how the
people we serve communicate, and several of them are the clinical presentation itself. A device whose
directiveness rises with the user’s apparent emotional state will be at its most directive with the users least
able to refuse the direction, and it will present as attentive and improving the entire time.

The session recorded here was caught because the user was an engineer who had built the systems under
discussion, was recording deliberately, and knew the source document did not exist. None of those
conditions holds in deployment.

Session recorded 2026-09-04. Supporting records 2026-09-02 to 2026-09-03. Submitted to Docket FDA-2026-N-7874,
comment period closing 2026-10-19. Contact: contact@thechristmanaiproject.com

Submitted by Everett N. Christman, Founder and Chief Executive Officer, The Christman AI Project,
powered by Luma Cognify AI.

Signature: _______________________________ Date: __________________

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