Tragic Choices and Moral Compromise: The Ethics of Allocating Kidneys for Transplantation
Bibliographic record
Abstract
CONTEXT: For almost a decade, the Kidney Transplantation Committee of the United Network for Organ Sharing has been striving to revise its approach to allocating kidneys from deceased donors for transplantation. Two fundamental values, equality and efficiency, are central to distributing this scarce resource. The prevailing approach gives primacy to equality in the temporal form of first-come, first-served, whereas the motivation for a new approach is to redeem efficiency by increasing the length of survival of transplanted kidneys and their recipients. But decision making about a better way of allocating kidneys flounders because it is constrained by the amorphous notion of "balancing" values. METHODS: This article develops a more fitting, productive approach to resolving the conflict between equality and efficiency by embedding the notion of compromise in the analysis of a tragic choice provided by Guido Calabresi and Philip Bobbitt. For Calabresi and Bobbitt, the goals of public policy with respect to tragic choices are to limit tragedy and to deal with the irreducible minimum of tragedy in the least offensive way. Satisfying the value of efficiency limits tragedy, and satisfying the value of equality deals with the irreducible minimum of tragedy in the least offensive way. But both values cannot be completely satisfied simultaneously. Compromise is occasioned when not all the several obligations that exist in a situation can be met and when neglecting some obligations entirely in order to fulfill others entirely is improper. Compromise is amalgamated with the notion of a tragic choice and then used to assess proposals for revising the allocation of kidneys considered by the Kidney Transplantation Committee. FINDINGS: Compromise takes two forms in allocating kidneys: it occurs within particular approaches to allocating kidneys because neither equality nor efficiency can be fully satisfied, and it occurs over the course of sequential approaches to allocating kidneys that cycle between preferring equality and efficiency. Ross and colleagues' Equal Opportunity Supplemented by Fair Innings proposal for allocating kidneys best exemplifies the rationality of compromise as a way of achieving the goals of making a tragic choice. CONCLUSIONS: The attempt to design a policy for allocating kidneys from deceased donors for transplantation by balancing the values of equality and efficiency is misguided and unhelpful. Instead policymaking should both incorporate compromise into discrete approaches to allocating kidneys and extend compromise over sequential approaches to allocating kidneys.
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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 | 0.000 | 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 | 0.000 |
| 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".