Medical AI, Categories of Value Conflict, and Conflict Bypasses
Bibliographic record
Abstract
It is becoming clear that, in the process of aligning AI with human values, one glaring ethical problem is that of value conflict. It is not obvious what we should do when two compelling values (such as autonomy and safety) come into conflict with one another in the design or implementation of a medical AI technology. This paper shares findings from a scoping review at the intersection of three concepts—AI, moral value, and health—that have to do with value conflict and arbitration. The paper looks at some important and unique cases of value conflict, and then describes three possible categories of value conflict: personal value conflict, interpersonal or intercommunal value conflict, and definitional value conflict. It then describes three general paths forward in addressing value conflict: additional ethical theory, additional empirical evidence, and bypassing the conflict altogether. Finally, it reflects on the efficacy of these three paths forward as ways of addressing the three categories of value conflict, and motions toward what is needed for better approaching value conflicts in medical AI.
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.
How this classification was reachedexpand
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".