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.

翻译：深度伪造是一种利用深度学习方法将他人的肖像叠加或生成到现有声音、图像或视频中的合成媒体。现有关于创作和传播深度伪造行为不当性的论述，主要聚焦于它们造成的伤害或所侵犯的非规范性利益。然而，这些方法无法解释为何深度伪造在既不造成伤害也不损害其他非规范性利益的情况下仍可能是不正当的。为解决这一问题，本文识别了一个被忽视的深层原因：深度伪造可能颠覆我们在掌握自身图像允许用途及身份治理方面的正当利益。我们认为，当深度伪造通过将我们的生物特征作为生成资源加以利用，从而篡夺我们决定自身行为来源的权威时，其行为即属不当。具体而言，我们拥有一种反对算法强制征用身份的特殊权利。通过区分艺术描绘等许可性挪用形式与不正当的算法模拟，本文进一步明确了该利益的边界范围。