Naming and Diffusing the <i>Understanding Objection </i>in Healthcare Artificial Intelligence
Bibliographic record
Abstract
Informed consent is often argued to be one of the more significant potential problems for the implementation and widespread onboarding of artificial intelligence (AI) and machine learning in healthcare decision-making. This is because of the concern revolving around whether, and to what degree, patients can understand what contributes to the decision-making process when an algorithm is involved. In this paper, I address what I call the Understanding Objection, which is the idea that AI systems will cause problems for the informational criteria involved in proper informed consent. I demonstrate that collaboration with clinicians in a human-in-the-loop partnership can alleviate these concerns around understanding, regardless how one conceptualizes the scope of understanding. Importantly, I argue that the human clinicians must be the second reader in the partnership to avoid institutional deference to the machine and best promote clinicians as the experts in the process.
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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 | 0.212 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.148 |
| Scholarly communication | 0.031 | 0.041 |
| Open science | 0.006 | 0.028 |
| Research integrity | 0.032 | 0.030 |
| Insufficient payload (model declined to judge) | 0.004 | 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".