Bibliographic record
Abstract
ChatGPT and other large language model (LLM) based chatbots have amassed millions of users in the past few years, prompting great interest in other LLM applications. In particular, there has been substantial research into the potential use of LLMs in mental health settings (Muetenda et al. 2025; Olawade et al. 2024). However, given that artificial intelligence (AI) has been found to demonstrate gender and racial biases (among other types) (Klein and D’Ignazio 2024), it is important to examine the ethical implications of such technologies. In this essay, I will examine the use of LLMs in therapeutic contexts through the lens of data feminism in AI (Klein and D’Ignazio 2024), a set of intersectional feminist principles introduced by Klein and D’Ignazio to challenge the power imbalances in data science and AI by exploring potential risks and offering suggestions to avoid them. It is important to note that the intersectional feminist lens is far from exhaustive; thus, future work should examine the use of LLMs in mental health care from other perspectives as well to prevent harm and ensure that the benefits of technological developments are spread equitably. I can easily imagine a future where LLMs are used to make mental health care more accessible and effective for everyone; I can just as easily imagine a future where they are weaponized to further subjugate disenfranchised communities. How do we steer towards the future we want? In this essay I hope to start exploring the answer to this question.
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.
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 | 0.129 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.043 |
| Scholarly communication | 0.016 | 0.040 |
| Open science | 0.005 | 0.028 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 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".