Researcher in ethics of AI and Privacy

Welcome to my site!

Jakob Mainz
I'm currently a Postdoctoral Fellow at Aarhus University. My research is primarily focused on the epistemological and normative questions related to Artificial Intelligence and decisional algorithms 
Recent paper

Big Data Analytics and How to Buy an Election

Co-authored with Jørn Sønderholm and Rasmus Uhrenfeldt

We show how to lawfully buy an election. The key things that make it possible to buy an election are the existence of public voter registration lists and the existence of Big Data Analytics that can predict how a given elector will vote in an election. Someone interested in buying an election can enter an employment contract with some of the opponent electors where these electors are paid to do a job that prevents them from voting. By purchasing access to public voter registration lists, it is possible to verify ex post whether the opponent electors have abstained. In the last two sections, we discuss several barriers that can undermine an attempt to buy an election in the manner we identify.

Recent paper

An Indirect Argument for the access theory of privacy

Winner of res publica's postgraduate paper prize.

In this paper, I offer an indirect argument for the Access Theory of privacy. First, I develop a new version of the rival Control Theory that is immune to all the classic objections against it. Second, I show that this new version of the Control Theory collapses into the Access Theory. I call the new version the ‘Negative Control Account’. Roughly speaking, the classic Control Theory holds that you have privacy if, and only if, you can control whether other people know personal information about you. Critics of the Control Theory often give counterexamples, where privacy is either not diminished even though the claimant has lost control, or where privacy is diminished even though the claimant is in control. I argue that none of these alleged counterexamples work against the Negative Control Account. However, this is not a victory for the control theorist, because the Negative Control Account collapses into the Access Theory. The paper thus adds to the recent trend in the literature of favoring the Access Theory over the Control Theory.

Recent paper

Too Much Info: Data Surveillance and Reasons to Favor the Control Account of the Right to Privacy

Co-authored with Rasmus Uhrenfeldt

In this paper, we argue that there is at least a pro tanto reason to favor the control account of the right to privacy over the access account of the right to privacy. This conclusion is of interest due to its relevance for contemporary discussions related to surveillance policies. We discuss several ways in which the two accounts of the right to privacy can be improved significantly by making minor adjustments to their respective definitions. We then test the improved versions of the two accounts on a test case, to see which account best explains the violation that occurs in the case. The test turns out in favor of the control account.

Recent paper

But anyone can mix their labor: a reply to Cheneval


Francis Cheneval has recently argued that people have property rights over personal data about themselves. Until now, the discussion on data ownership has primarily been a discussion among legal theorists and economists. Cheneval contribution to the discussion is a very welcome input from academic philosophy. Cheneval attempts to reach his conclusion through two distinct strategies. One strategy is to reach the conclusion through a Lockean inspired libertarian rights-based theory of property. The second strategy is to reach his conclusion through a Rawlsian account of distributive justice. According to Cheneval, his conclusion can be reached both ways. In this reply, I will focus exclusively on Cheneval argument that people have Lockean inspired libertarian property rights over personal data. I will offer an objection, which– if correct –demonstrates how Cheneval Lockean argument runs into a dilemma.

  • Link to paper

    https://www.tandfonline.com/doi/abs/10.1080/13698230.2020.1764786?journalCode=fcri20

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