Abstract
AbstractThis paper focuses on the use of ‘black box’ AI in medicine and asks whether the physician needs to disclose to patients that even the best AI comes with the risks of cyberattacks, systematic bias, and a particular type of mismatch between AI’s implicit assumptions and an individual patient’s background situation. Pace current clinical practice, I argue that, under certain circumstances, these risks do need to be disclosed. Otherwise, the physician either vitiates a patient’s informed consent or violates a more general obligation to warn him about potentially harmful consequences. To support this view, I argue, first, that the already widely accepted conditions in the evaluation of risks, i.e. the ‘nature’ and ‘likelihood’ of risks, speak in favour of disclosure and, second, that principled objections against the disclosure of these risks do not withstand scrutiny. Moreover, I also explain that these risks are exacerbated by pandemics like the COVID-19 crisis, which further emphasises their significance.
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Human-Computer Interaction,Philosophy
Reference58 articles.
1. Aczon M, Ledbetter D, Ho L, Gunny A, Flynn A, Williams J, Wetzel R (2017) Dynamic mortality risk predictions in pediatric critical care using recurrent neural networks. arXiv preprint arXiv:170106675
2. Adadi A, Berrada M (2018) Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6:52138–52160. https://doi.org/10.1109/access.2018.2870052
3. Argaw ST, Bempong N-E, Eshaya-Chauvin B, Flahault A (2019) The state of research on cyberattacks against hospitals and available best practice recommendations: a scoping review. BMC Med Inform Decis Mak 19:1–11. https://doi.org/10.1186/s12911-018-0724-5
4. Arshadi K, Salem M, Collins J, Yuan JS, Chakrabarti D (2020) DeepMalaria: artificial intelligence driven discovery of potent antiplasmodials. Front Pharmacol 10:1526. https://doi.org/10.3389/fphar.2019.01526
5. Bates DW, Saria S, Ohno-Machado L, Shah A, Escobar G (2014) Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Aff 33:1123–1131. https://doi.org/10.1377/hlthaff.2014.0041
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