RT Journal Article
SR Electronic
T1 Design publicity of black box algorithms: a support to the epistemic and ethical justifications of medical AI systems
JF Journal of Medical Ethics
JO J Med Ethics
FD BMJ Publishing Group Ltd and Institute of Medical Ethics
SP 492
OP 494
DO 10.1136/medethics-2021-107482
VO 48
IS 7
A1 Andrea Ferrario
YR 2022
UL http://jme.bmj.com/content/48/7/492.abstract
AB In their article ‘Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI’, Durán and Jongsma discuss the epistemic and ethical challenges raised by black box algorithms in medical practice. The opacity of black box algorithms is an obstacle to the trustworthiness of their outcomes. Moreover, the use of opaque algorithms is not normatively justified in medical practice. The authors introduce a formalism, called computational reliabilism, which allows generating justified beliefs on the algorithm reliability and trustworthy outcomes of artificial intelligence (AI) systems by means of epistemic warrants, called reliability indicators. However, they remark the need for reliability indicators specific to black box algorithms and that justified knowledge is not sufficient to justify normatively the actions of the physicians using medical AI systems. Therefore, Durán and Jongsma advocate for a more transparent design and implementation of black box algorithms, providing a series of recommendations to mitigate the epistemic and ethical challenges behind their use in medical practice. In this response, I argue that a peculiar form of black box algorithm transparency, called design publicity, may efficiently implement these recommendations. Design publicity encodes epistemic, that is, reliability indicators, and ethical recommendations for black box algorithms by means of four subtypes of transparency. These target the values and goals, their translation into design requirements, the performance and consistency of the algorithm altogether. I discuss design publicity applying it to a use case focused on the automated classification of skin lesions from medical images.