Bibliographic record
Abstract
There is growing interest within the medical sector about the diagnostic potential of voice analysis-based artificial intelligence (AI) for monitoring mental health, such as depression detection. However, insufficient attention has been paid to the societal consequences of such technologies rendering depression and similar disabilities into purely technical problems. We provide a critical case study of Sonde Health, a Boston-based startup that purports to offer “objective” depression detection and monitoring via its Mental Fitness app that extracts and analyzes the acoustic features of the user’s voice. Using a critical disability studies lens, we conducted a textual analysis of the publicly available developer documentation for Sonde’s application programming interface, examining each of these acoustic features (“vocal biomarkers”), and problematizing Sonde’s claims that these vocal biomarkers are objective universal indicators of depression. Through our case study, we identify and illustrate three hegemonic norms that contribute to troubling social implications of the technology: the fallacy that complex psychometrics can be meaningfully flattened into a single encompassing score, the aesthetic of “objectivity”, and the presumptive universalizing of easily-available voice data sets. We discuss how all three are tied up in the legacy of eugenics and reflect a fundamental mismatch in values between mainstream AI technology and the humanistic requirements of mental health care.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.092 | 0.049 |
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".