REFERENCES
- Bak, M. A. R. 2022. Computing fairness: Ethics of modeling and simulation in public health. Simulation 98 (2):103–11. doi:10.1177/0037549720932656.
- Bak, M. A. R., V. I. Madai, M. C. Fritzsche, M. T. Mayrhofer, and S. McLennan. 2022. You can’t have AI both ways: Balancing health data privacy and access fairly. Frontiers in Genetics 13:929453. doi:10.3389/fgene.2022.929453.
- Carusi, A. 2008. Scientific visualisations and aesthetic grounds for trust. Ethics and Information Technology 10:243–54. doi:10.1007/s10676-008-9159-5.
- Cerrato, P., J. Halamka, and M. Pencina. 2022. A proposal for developing a platform that evaluates algorithmic equity and accuracy. BMJ Health & Care Informatics 29 (1):1.
- Cohen, I. G. 2023. What should ChatGPT mean for bioethics? The American Journal of Bioethics 23 (10):8–16. doi:10.1080/15265161.2023.2233357.
- Floridi, L. 2010. Ethics after the information revolution. In The Cambridge handbook of information and computer ethics, ed. L. Floridi, 3–19. Cambridge: Cambridge University Press.
- Lauritzen, P. 2008. Visual bioethics. The American Journal of Bioethics 8 (12):50–6. doi:10.1080/15265160802559146.
- Mills, C. 2008. Images and emotion in abortion debates. The American Journal of Bioethics 8 (12):61–2. doi:10.1080/15265160802559187.
- Nussbaum, M. C. 2015. Transitional anger. Journal of the American Philosophical Association 1 (1):41–56. doi:10.1017/apa.2014.19.
- Ricci Lara, M. A., R. Echeveste, and E. Ferrante. 2022. Addressing fairness in artificial intelligence for medical imaging. Nature Communications 13 (1):4581. doi:10.1038/s41467-022-32186-3.