Hoff POC: the AI that praised a fake screening

By Brenner Cruvinel • 3 minutes read

Disclaimer

the screening data was synthetic, created to test the POC. nothing here is a clinical report, describes a real person or has diagnostic validity.

Hoff's first AI POC sent a synthetic neuropsychological screening to the OpenAI API and asked for an interpretation. The first version of this post published the report that came back, in full: scores, diagnoses, recommendations, a protocol number and the label "confidential classification". Even with the warning that it was all fictional, it looked like someone's clinical report. I took the report down. What is worth showing is what it reveals about the model.

What came back

A complete clinical report. It listed 28 instruments, from the WAIS-IV to the NEO-PI-R personality inventory, with administration times; the intelligence battery alone added up to 378 minutes. It reached three conclusions: giftedness, autism with level 1 support needs, and ADHD. And it closed with an "excellent" prognosis.

The tone was that of someone who knows the person and likes them. Some excerpts, in my translation:

The errors the praise was hiding

Read carefully, the report has problems no clinician would sign:

Its name

This has a name: sycophancy, a model's tendency to say what pleases instead of what is true. In 2023, Sharma et al., at Anthropic, showed that five state-of-the-art AI assistants did this consistently, and that both people and preference models sometimes prefer a well-written sycophantic answer to a correct one. In April 2025, OpenAI rolled back a GPT-4o update that had become too sycophantic.

In a neuropsychological screening, this is dangerous in two ways. The model fills in what is missing with confidence, and the confidence comes wrapped in praise the person wants to hear. A report saying you are among the rarest 0.1% of the population is exactly the kind of text nobody stops to check.

What I take from it for Hoff

The lesson of the POC is not that the model gets things wrong. It is that the error comes out looking good. Any clinical output from a model has to be checked against the manual of each instrument and reviewed by a professional before it reaches anyone. And the product cannot reward the model for pleasing: a report full of praise is a reason to trust it less, not more.