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This paper argues that the wrongfulness of deepfakes extends beyond causing harm or violating interests, focusing on the subversion of individuals' authority over their image and identity governance. It posits that deepfakes algorithmically conscript identity by exploiting biometric features, thereby usurping the individual's right to determine the provenance of their own agency. The paper differentiates this algorithmic conscription from permissible forms of image appropriation like artistic depiction, refining the scope of legitimate interest.
Deepfakes aren't just harmful; they steal your right to control how your image is used and who gets to simulate you.
Deepfakes are synthetic media that superimpose or generate someone’s likeness onto pre-existing sound, images, or videos using deep learning methods. Existing accounts of the wrongs involved in creating and distributing deepfakes focus on the harms they cause or the non-normative interests they violate. However, these approaches do not explain how deepfakes can be wrongful even when they cause no harm or set back any other non-normative interest. To address this issue, this paper identifies a neglected reason why deepfakes are wrong: they can subvert our legitimate interests in having authority over the permissible uses of our image and the governance of our identity. We argue that deepfakes are wrong when they usurp our authority to determine the provenance of our own agency by exploiting our biometric features as a generative resource. In particular, we have a specific right against the algorithmic conscription of our identity. We refine the scope of this interest by distinguishing between permissible forms of appropriation, such as artistic depiction, from wrongful algorithmic simulation.