Deepfakes at face value: image and authority

Journal article

Deepfakes are synthetic media that superimpose or generate someone’s likeness on to 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.

Authors: Kirkpatrick, JR

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer
• Journal: AI and Society
• Publication date: 2026-04-11
• Acceptance date: 2026-03-29
• DOI: 10.1007/s00146-026-03018-5
• EISSN: 1435-5655
• ISSN: 0951-5666
• Language: English
• Keywords: AI ethics; authority interests; deepfakes; deep learning; non-consensual intimate images