REFERENCES
- Alvarado, R. 2020. Opacity artificial intelligence, machine learning, big data and democratic processes. In Big data and democracy, ed. K. Macnish and J. Galliott. Edinburgh: Edinburgh University Press.
- Beauchamp, T., and J. Childress. 2019. Principles of biomedical ethics. 8th ed. New York: Oxford University Press.
- Crawford, K. 2021. Time to regulate AI that interprets human emotions. Nature 592:167. doi:10.1038/d41586-021-00868-5.
- Fricker, M. 2017. Evolving concepts of epistemic injustice. In Routledge handbook of epistemic injustice, ed. I. J. Kidd, J. Medina, and G. Pohlhaus Jr, 53–60. New York: Routledge.
- Krupinski, E. A., R. M. Levenson, V. Navarro, and E. A. Wasserman. 2016. The potential of pigeons as surrogate observers in medical image perception studies. Proc. SPIE 9787, Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, 97870J; doi:10.1117/12.2207774.
- Laacke, S., Mueller, R., G. Schomerus, and S. Salloch. 2021. Artificial intelligence, social media and depression. A new concept of health-related digital autonomy. The American Journal of Bioethics 21 (7):4–20. doi:10.1080/15265161.2020.1863515.
- London, A. J. 2019. Artificial intelligence and black‐box medical decisions: Accuracy versus explainability. Hastings Center Report 49 (1):15–21. doi:10.1002/hast.973.
- Lynch, S. K., A. Shah, J. C. Folk, X. Wu, and M. D. Abramoff. 2017. Catastrophic failure in image-based convolutional neural network algorithms for detecting diabetic retinopathy. Investigative Ophthalmology & Visual Science 58 (8):3776.
- Sun, Y., X. Huang, D. Kroening, J. Sharp, M. Hill, and R. Ashmore. 2018. Testing deep neural networks. arXiv preprint arXiv:1803.04792.
- Suresh, H., and J. V. Guttag. 2019. A framework for understanding unintended consequences of machine learning. arXiv preprint arXiv:1901.10002.
- Zuboff, S. 2019. The age of surveillance capitalism: The fight for a human future at the new frontier of power. New York: Public Affairs.