New article in Big Data & Society (open access): “Predictive Privacy: Collective Data Protection in the Context of AI and Big Data”
I am excited to announce the release of my latest article on predictive privacy. In this paper, I tackle the challenge posed by big data and artificial intelligence, which allow predictions to be made about individuals based on anonymous data from many people.
Going beyond my 2021 article in Ethics & Information Theory, this paper elaborates more closely on the philosophical and legal implications of predictive privacy. It presents a refined definition of the term and argues for the construction of predictive privacy as a new protected good.
Predictive analytics has significant potential for abuse, leading to social inequality, discrimination, and exclusion. Current data protection laws do not adequately address these risks, leaving the use of anonymized mass data largely unregulated. My paper introduces the concept of ‘predictive privacy’ as a data protection approach to counter these risks.
“Social Media Advertising for Clinical Studies: Ethical and Data Protection Implications of Online Targeting” (with Theresa Willem)
In this article, we discuss the data ethics and privacy implications of online advertising on social media.
Using the example of online advertising for clinical trials (research on new drugs or therapies), our study shows that personalized advertising allows platforms such as Google or Facebook to train predictive models for the diseases in question. These can be applied to any people, including users who have never seen the ads themselves, to predict whether they suffer from the disease in question.
Abstract: Social media advertising has revolutionised the advertising world by providing data-driven targeting methods. One area where social media advertising is just gaining a foothold is in the recruitment of clinical study participants. Here, as everywhere, social media advertising promises more yield per money spent because the technology can better reach highly specialised groups. In this article, we point out severe societal risks posed by advertising for clinical studies on social media. We show that social media advertising for clinical studies in many cases violates the privacy of individual users (R1), creates collective privacy risks by helping platform companies train predictive models of medical information that can be applied to all their users (R2), exploits the weaknesses of existing guidelines in (biomedical) research ethics (R3) and is detrimental to the quality of (biomedical) research (R4).
More interesting articles:
Okt 12, 2022 - Prädiktive Privatheit: Kollektiver Datenschutz im Kontext von Big Data und KI (in German)
Sept 8, 2022 - Deutschlandfunk Nova – Broadcast Lecture Hall (in German)
Aug 29, 2022 - Live on radio WDR5 – The Philosophical Radio (in German)