Fair by chance? On the use of algorithms in therapeutic decisions
Bibliographic Data
| ID | 21514846 |
|---|---|
| Authors | Sune Holm (0000-0002-3812-7942, University of Copenhagen, Frederiksberg, Denmark, corresponding author) |
| Year | 2026 |
| Volume | 52 |
| Issue | 7 |
| Pages | 445-448 |
| Publication date | 2026-07-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Medical Ethics (JOURNAL) |
| Journal identifiers | ISSN: 0306-6800 • E-ISSN: 1473-4257 |
| Publisher | BMJ (PUBLISHER • GB) |
| DOI | 10.1136/jme-2025-110819 |
| PMID | 40316444 |
| OpenAlex | W4410029366 |
| Language | EN |
| Citations received | 1 |
| References cited | 13 |
Predictive tools made possible by advances in machine learning techniques may help clinicians make more accurate decisions about who should be allocated costly therapies, such as immunotherapy, which only work on a relatively low proportion of patients. In this article, I argue that a fair decision procedure must recognise each patients’ chance of responding well. To do so, the procedure should not apply a fixed threshold to probability scores. Rather, each patient should be given a chance of being allocated the therapy matching her probability score. An important consequence of this conclusion is that the fair use of algorithmic scores may not guarantee that the therapy is allocated to those who will respond well to the highest possible degree
Machine learning · Biomedical Ethics and Regulation · Computer Science · Health Systems, Economic Evaluations, Quality of Life · Healthcare cost, quality, practices · Medicine · Artificial Intelligence
V—Fairness
Enabling Fairness in Healthcare Through Machine Learning
Fair equality of chances for prediction-based decisions
On the site of predictive justice
The Fairness in Algorithmic Fairness
Broomean(ish) Algorithmic Fairness
On statistical criteria of algorithmic fairness
Reconciling Algorithmic Fairness Criteria
| Citations per year | 1 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |