Agree to disagree
The symmetry of burden of proof in human–AI collaboration
Bibliographic Data
| ID | 21515225 |
|---|---|
| Authors | Karin R Jongsma (0000-0001-8135-6786, Utrecht University, corresponding author), Mary Sand (0000-0001-8167-4581, Delft University of Technology) |
| Year | 2022 |
| Volume | 48 |
| Issue | 4 |
| Pages | 230-231 |
| Publication date | 2022-04-01 |
| 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/medethics-2022-108242 |
| PMID | 35321904 |
| OpenAlex | W4221022120 |
| Language | EN |
| Citations received | 2 |
| References cited | 7 |
Burden of proof · Data science · Political science · Proof of concept · Artificial Intelligence in Healthcare and Education · Computer Science · Ethics and Social Impacts of AI · Explainable Artificial Intelligence (XAI · Law · Mathematics
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Explainability for artificial intelligence in healthcare
Responsibility, second opinions and peer-disagreement
Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI
Responsibility beyond design
Four Responsibility Gaps with Artificial Intelligence
Understanding from Machine Learning Models
| Citations per year | 1 |
|---|---|
| Citation span | 2024 - 2025 (2) |
| Highly cited | No |