Beyond Doomsday Fears: Why We Need to Consider the Potential Harms of AI Psychotherapy
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.178 | 0.335 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.011 | 0.088 |
| Scholarly communication | 0.016 | 0.048 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.038 | 0.060 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".