Designing Worlds, Worlding Design: The Politics of Value Creation in Artificial Intelligence for Health
Bibliographic record
Abstract
In this dissertation I examine the politics of value creation in the design and governance of health-related artificial intelligence (AI). Drawing on perspectives from the interdisciplinary field of Science and Technology Studies, I aim to advance a clearer understanding of how to promote collective public value in data-intensive health systems. Three research papers are presented: two qualitative studies focused on an empirical case involving the commercialization of a hospital-developed AI technology, and one conceptually-oriented structured literature review. In the first paper, I critically engage with process-oriented ‘lifecycle’ approaches to the responsible development and oversight of AI systems in health care. Through the empirical case, I suggest that a shift in focus to ‘events’ can direct attention to specific, temporally-bound junctures that have disproportionate impacts on development and use. In the second paper, I examine the valuation practices that inform different data monetization strategies in the context of the same empirical case. I especially engage with theoretical perspectives on assetization, which I suggest can help elucidate the potential role of AI technologies in emerging health data markets. In the third paper, I review the scholarly and grey literature on algorithmic accountability and propose five normative logics characterizing its application in health policy and governance. In doing so, I aim to clarify the myriad ambitions of accountability regimes in practice, and the associated expectations of those tasked with pursuing or evaluating them. I conclude the dissertation with a discussion of ‘worlding’, where value-laden practices of design and governance bring certain realities into being, and may therefore also be capable of generating alternative, more inclusive health futures. I offer three focal points in particular that can sensitize practices of responsible design and governance to multiple worlds.
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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 | 0.040 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.087 |
| Scholarly communication | 0.025 | 0.022 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".