Naming and Diffusing the <i>Understanding Objection </i>in Healthcare Artificial Intelligence
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
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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