The prospects of using AI in euthanasia and physician-assisted suicide: a legal exploration
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The Netherlands was the first country to legalize euthanasia and physician-assisted suicide. This paper offers a first legal perspective on the prospects of using AI in the Dutch practice of euthanasia and physician-assisted suicide. It responds to the Regional Euthanasia Review Committees’ interest in exploring technological solutions to improve current procedures. The specific characteristics of AI – the capability to process enormous amounts of data in a short amount of time and generate new insights in individual cases – may for example alleviate the increased workload of review committees due to the continuous increase of euthanasia cases. The paper considers three broad categories for the use of AI in the Dutch euthanasia practice: (1) the physician’s assessment of euthanasia requests, (2) the actual execution of euthanasia, and (3) the retrospective reviews of cases by the Regional Euthanasia Review Committees. Exploring the legal considerations around each avenue, both in the EU AI Act and the Dutch legal framework, this paper aims to facilitate the societal discussion on the role of technology in such deeply human decisions. This debate is equally relevant to other countries that legalized euthanasia (e.g. Belgium and Canada) or physician-assisted suicide (e.g. Switzerland and numerous states in the US).
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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 it