Deep opacity undermines data protection and explainable artificial intelligence - Müller, Vincent C. (AISB Convention 2021: Communication and Conversations, 2021)
It is known that big data analytics and AI pose another threat to privacy, and it is known that there is some kind of ‘black box problem’ in AI. I propose that (1) the ‘black box’ becomes the ‘black box problem’ in a context of justification for judgments and actions, crucially in the context of privacy. (2) This will suggest distinguishing two kinds of classic opacity and introducing a third: The subjects may not know what the system does (‘shallow opacity’), the analysts may not know what the system does (‘standard black box opacity’), or even the analysts cannot possibly know what the system might do (‘deep opacity’). (3) If the agents, data subjects as well as analytics experts, operate under opacity, then they cannot provide some of the justifications for judgments that are necessary to protect privacy – e.g. they cannot give “informed consent” or assert “anonymity”. It follows from (2) and (3) that agents in big data analytics and AI, often cannot make the judgments needed to protect privacy. So big data analytics makes the privacy problems worse, and the remedies less effective. Closing, I provide a brief outlook on technical ways to handle this situation.
CRIS-Team Scopus Importer:2022-06-20
Keywords: Big data analytics; Black box problem; Deep opacity; Explainable AI; Justification; Opacity; Privacy