Beyond Doomsday Fears: Why We Need to Consider the Potential Harms of AI Psychotherapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There is increased enthusiasm about the use of Artificial Intelligence (AI) technologies in psychotherapy. Notably, AI psychotherapy chatbots are increasing in popularity, especially since the US Food and Drug Administration (FDA) gave one of these apps breakthrough device designation. This article raises concerns about the lack of consideration of potential harms of this technology for clinical trial participants, and current and future users. We outline what these harms might be, by turning to the Belmont Report and the existing literature on harms of (typical) psychotherapy and conclude with two recommendations. Note that our goal is not to articulate doomsday fears regarding the use of AI in psychotherapy contexts; rather we offer a constructive proposal in thinking about the potential harms of these tools and invite clinicians, patients, developers, researchers, policymakers and funding agencies to work together to augment the benefits of these tools and minimize their potential harms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it