Several leading theorists embrace the Simple Desert Account of Discrimination. This account involves two claims: it claims that a mismatch between what people deserve, on the one hand, and what they get, on the other hand, is (a) integral to discrimination, and (b) wrong. I shall query (a). First, I challenge what I see as the principal, positive argument for the Simple Desert Account. Second, in some cases wrongful discrimination brings about a better match between desert and what people get. (...) Situations in which this could be the case include those where: the discriminatee is a serial discriminator herself; the person wrongfully discriminated in favour of deserves greater advantages than she enjoys; by engaging in wrongful discrimination the discriminator reduces her moral deservingness and thereby the gap between her level of deservingness and her otherwise unfittingly low level of advantage. (shrink)

Recent technological innovation has made video doctoring increasingly accessible. This has given rise to Deepfake Pornography, an emerging phenomenon in which Deep Learning algorithms are used to superimpose a person’s face onto a pornographic video. Although to most people, Deepfake Pornography is intuitively unethical, it seems difficult to justify this intuition without simultaneously condemning other actions that we do not ordinarily find morally objectionable, such as sexual fantasies. In the present article, I refer to this contradiction as the pervert’s dilemma. (...) I propose that the method of Levels of Abstraction, a philosophical mode of enquiry inspired by Formal Methods in computer science, can be employed to formulate at least one possible solution to the dilemma. From this perspective, the permissibility of some actions appears to depend on the degree to which they are abstracted from their natural context. I conclude that the dilemma can only be solved when considered at low levels of abstractions, when Deepfakes are situated in the macro-context of gender inequality. (shrink)