Political deepfakes have prompted outcry about the diminishing trustworthiness of visual depictions, and the epistemic and political threat this poses. Yet this new technique is being used overwhelmingly to create pornography, raising the question of what, if anything, is wrong with the creation of deepfake pornography. Traditional objections focusing on the sexual abuse of those depicted fail to apply to deepfakes. Other objections—that the use and consumption of pornography harms the viewer or other (non-depicted) individuals—fail to explain the objection that (...) a depicted person might have to the creation of deepfake pornography that utilises images of them. My argument offers such an explanation. It begins by noting that the creation of sexual images requires an act of consent, separate from any consent needed for the acts depicted. Once we have separated these out, we can see that a demand for consent can arise when a sexual image is of us, even when no sexual activity was actually engaged in, as in the case of deepfake pornography. I then demonstrate that there are two ways in which an image can be ‘of us’, both of which can exist in the case of deepfakes. Thus, I argue: if a person, their likeness, or their photograph is used to create pornography, their consent is required. Whenever the person depicted does not consent (or in the case of children, can’t consent), that person is wronged by the creation of deepfake pornography and has a claim against its production. (shrink)

Morality bears on what we should forget. Some aspects of our identity are meant to be forgotten and there is a distinctive harm that accompanies the permanence of some content about us, content that prompts a duty to forget. To make the case that forgetting is an integral part of our moral duties to others, the paper proceeds as follows. In §1, I make the case that forgetting is morally evaluable and I survey three kinds of forgetting: no-trace forgetting, archival (...) forgetting, and siloing. In §2, I turn to how we practice these forms of forgetting in our everyday lives and the goods these practices facilitate by drawing on examples ranging from the expunging of juvenile arrest records to the right to privacy. In §3, I turn to how my account can help us both recognize and address a heretofore neglected source of harm caused by technology and big data. In §4, I end by addressing the concern that we lack control over forgetting and thus can't be required to forget. I argue this challenge can be answered, but there’s a harder challenge that can’t. Forgetting is under threat. To address this challenge and preserve forgetting, we must change the world. (shrink)

Deepfakes pose a multi-faceted threat to the acquisition of knowledge. It is widely hoped that technological solutions—in the form of artificially intelligent systems for detecting deepfakes—will help to address this threat. I argue that the prospects for purely technological solutions to the problem of deepfakes are dim. Especially given the evolving nature of the threat, technological solutions cannot be expected to prevent deception at the hands of deepfakes, or to preserve the authority of video footage. Moreover, the success of such (...) technologies depends on institutional trust that is in short supply. Finally, outsourcing the discrimination between the real and the fake to automated, largely opaque systems runs the risk of undermining epistemic autonomy. (shrink)

Deepfakes are extremely realistic audio/video media. They are produced via a complex machine-learning process, one that centrally involves training an algorithm on hundreds or thousands of audio/video recordings of an object or person, S, with the aim of either creating entirely new audio/video media of S or else altering existing audio/video media of S. Deepfakes are widely predicted to have deleterious consequences (principally, moral and epistemic ones) for both individuals and various of our social practices and institutions. In this introduction (...) to the Topical Collection, I first survey existing philosophical research on deepfakes (Sects. 2 and 3). I then give an overview of the papers that comprise the Collection (Sect. 4). Finally, I conclude with remarks on a line of argument made in a number of papers in the Topical Collection: that deepfakes may cause their own demise (Sect. 5). (shrink)

Deepfakes raise various concerns: risks of political destabilization, depictions of persons without consent and causing them harms, erosion of trust in video and audio as reliable sources of evidence, and more. These concerns have been the focus of recent work in the philosophical literature on deepfakes. However, there has been almost no sustained philosophical analysis of deepfakes from the perspective of concerns about honesty and dishonesty. That deepfakes are potentially deceptive is unsurprising and has been noted. But under what conditions (...) does the use of deepfakes fail to be honest? And which human agents, involved in one way or another in a deepfake, fail to be honest, and in what ways? If we are to understand better the morality of deepfakes, these questions need answering. Our first goal in this paper, therefore, is to offer an analysis of paradigmatic cases of deepfakes in light of the philosophy of honesty. While it is clear that many deepfakes are morally problematic, there has been a rising counter-chorus claiming that deepfakes are not essentially morally bad, since there might be uses of deepfakes that are not morally wrong, or even that are morally salutary, for instance, in education, entertainment, activism, and other areas. However, while there are reasons to think that deepfakes can supply or support moral goods, it is nevertheless possible that even these uses of deepfakes are dishonest. Our second goal in this paper, therefore, is to apply our analysis of deepfakes and honesty to the sorts of deepfakes hoped to be morally good or at least neutral. We conclude that, perhaps surprisingly, in many of these cases the use of deepfakes will be dishonest in some respects. Of course, there will be cases of deepfakes for which verdicts about honesty and moral permissibility do not line up. While we will sometimes suggest reasons why moral permissibility verdicts might diverge from honesty verdicts, we will not aim to settle matters of moral permissibility. (shrink)

In this paper, I draw a distinction between two types of deepfake, and unpack the deceptive strategies that are made possible by the second. The first category, which has been the focus of existing literature on the topic, consists of those deepfakes that act as a fabricated record of events, talk, and action, where any utterances included in the footage are not addressed to the audience of the deepfake. For instance, a fake video of two politicians conversing with one another. (...) The second category consists of those deepfakes that direct an illocutionary speech act - such as a request, injunction, invitation, or promise - to an addressee who is located outside of the recording. For instance, fake footage of a company director instructing their employee to make a payment, or of a military official urging the populace to flee for safety. Whereas the former category may deceive an audience by giving rise to false beliefs, the latter can more directly manipulate an agent's actions: the speech act's addressee may be moved to accept an invitation or a summons, follow a command, or heed a warning, and in doing so further a deceiver's unethical ends. (shrink)

Deepfakes are AI-generated media. When produced competently, they are near-indistinguishable from genuine recordings and may mislead viewers about the actions of the individuals they depict. For this reason, it is thought to be only a matter of time before deepfakes have deleterious consequences for democratic procedures, elections in particular. But this pessimistic view about deepfakes and their relation to democracy is flawed whether it means to pick out current deepfakes or future ones. Rather than advocating for an optimistic view in (...) its place, I outline the opposite: a nihilistic account of deepfakes and their relation to democracy. On the nihilistic view, the harms that deepfakes pose for democracy are significantly more serious than those implied by the pessimistic view. Nihilism says that the real threat that deepfakes pose for democracy is that their existence counts against reforming current politics to be more truth-oriented. (shrink)

The call to make artificial intelligence (AI) more democratic, or to “democratize AI,” is sometimes framed as a promising response for mitigating algorithmic injustice or making AI more aligned with social justice. However, the notion of “democratizing AI” is elusive, as the phrase has been associated with multiple meanings and practices, and the extent to which it may help mitigate algorithmic injustice is still underexplored. In this paper, based on a socio-technical understanding of algorithmic injustice, I examine three notable notions (...) of democratizing AI and their associated measures—democratizing AI use, democratizing AI development, and democratizing AI governance—regarding their respective prospects and limits in response to algorithmic injustice. My examinations reveal that while some versions of democratizing AI bear the prospect of mitigating the concern of algorithmic injustice, others are somewhat limited and might even function to perpetuate unjust power hierarchies. This analysis thus urges a more fine-grained discussion on how to democratize AI and suggests that closer scrutiny of the power dynamics embedded in the socio-technical structure can help guide such explorations. (shrink)

Generative artificial intelligence has given us synthetic media that are increasingly easy to create and increasingly hard to distinguish from photographs and videos. Whereas an existing literature has been concerned with how these new media might make a difference for would-be knowers—the viewers of photographs and videos—I advance a thesis about how they will make a difference for would-be communicators—those who embed photos and videos in their speech acts. I claim that the presence of these media in our information environment (...) reduces our ability to show one another things, even as it may increase our resources for telling. And I argue that this has consequences beyond the disruption of knowledge acquisition; showing is a way that we preserve relational equality through superficial asymmetries in political communication, and thereby express respect for our audiences. If synthetic media reduce our options for showing, they then interfere in the way that we manage our relationships in the context of collective political action. (shrink)