Experiments with Social Good: Feminist Critiques of Artificial Intelligence in Healthcare in India
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
In contemporary India, AI-enabled automated diagnostic models are beginning to control who gets access to what kind of medical care, with the most invasive systems being aimed at underserved communities. I critically question the dominant narrative of “AI for social good” that has been widely adopted by various stakeholders in the healthcare industry towards solving development challenges through the introduction of AI applications targeted towards the sick-poor. Using feminist theory, I argue that AI systems should not be seen as neutral products but complex sociotechnical processes embedded with gendered knowledge and labor. I analyze the layers of expropriation and experimentation that come into play when AI technologies become a method of using diverse bodies and medical records of the sick-poor as data to train proprietary AI algorithms at a low cost in the absence of effective state regulatory mechanisms. I posit that an overwhelming focus on “spectacular technologies” such as AI derails public efforts from solving the actual needs of populations targeted by the “AI for social good” narrative, and from the development of sustainable, responsible, situated healthcare solutions. Lastly, I offer social and policy recommendations that would enable us to envision inclusive feminist futures in which we understand and prioritize the needs of underserved populations over capitalist market logics in the development, deployment, and regulation of AI systems.
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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 | 0.004 | 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 | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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