That photography is a supremely realistic medium may be the commonsense view, but—as Edward Steichen reminds us—it is by no means universal. Dissenters note how unlike reality a photograph is and how unlikely we are to confuse the one with the other. They point to “distortions” engendered by the photographic process and to the control which the photographer exercises over the finished product, the opportunities he enjoys for interpretation and falsification. Many emphasize the expressive nature of the medium, observing that (...) photographs are inevitably colored by the photographer’s personal interests, attitudes, and prejudices.1 Whether any of these various considerations really does collide with photography’s claim of extraordinary realism depends, of course, on how that claim is to be understood.Those who find photographs especially realistic sometimes think of photography as a further advance in a direction which many picture makers have taken during the last several centuries, as a continuation or culmination of the post-Renaissance quest for realism.2 There is some truth in this. Such earlier advances toward realism include the development of perspective and modeling techniques, the portrayal of ordinary and incidental details, attention to the effects of light, and so on. From its very beginning, photography mastered perspective . Subtleties of shading, gradations of brightness nearly impossible to achieve with the brush, became commonplace. Photographs include as a matter of course the most mundane details of the scenes they portray—stray chickens, facial warts, clutters of dirty dishes. Photographic images easily can seem to be what painters striving for realism have always been after. 2. See André Bazin, “The Ontology of the Photographic Image,” What is Cinema?, trans. Hugh Gray, vol. 1 , p. 12; all further references to this work, abbreviated “OPI,” will be included in the text. See also Rudolf Arnheim, “Melancholy Unshaped,” Toward a Psychology of Art: Collected Essays , p. 186. Kendall L. Walton is professor of Philosophy at the University of Michigan. He is currently completing a book on representation in the arts. (shrink)

Deepfake technology presents significant ethical challenges. The ability to produce realistic looking and sounding video or audio files of people doing or saying things they did not do or say brings with it unprecedented opportunities for deception. The literature that addresses the ethical implications of deepfakes raises concerns about their potential use for blackmail, intimidation, and sabotage, ideological influencing, and incitement to violence as well as broader implications for trust and accountability. While this literature importantly identifies and signals the potentially (...) far-reaching consequences, less attention is paid to the moral dimensions of deepfake technology and deepfakes themselves. This article will help fill this gap by analysing whether deepfake technology and deepfakes are intrinsically morally wrong, and if so, why. The main argument is that deepfake technology and deepfakes are morally suspect, but not inherently morally wrong. Three factors are central to determining whether a deepfake is morally problematic: whether the deepfaked person would object to the way in which they are represented; whether the deepfake deceives viewers; and the intent with which the deepfake was created. The most distinctive aspect that renders deepfakes morally wrong is when they use digital data representing the image and/or voice of persons to portray them in ways in which they would be unwilling to be portrayed. Since our image and voice are closely linked to our identity, protection against the manipulation of hyper-realistic digital representations of our image and voice should be considered a fundamental moral right in the age of deepfakes. (shrink)

Deepfakes are realistic videos created using new machine learning techniques rather than traditional photographic means. They tend to depict people saying and doing things that they did not actually say or do. In the news media and the blogosphere, the worry has been raised that, as a result of deepfakes, we are heading toward an “infopocalypse” where we cannot tell what is real from what is not. Several philosophers have now issued similar warnings. In this paper, I offer an analysis (...) of why deepfakes are such a serious threat to knowledge. Utilizing the account of information carrying recently developed by Brian Skyrms, I argue that deepfakes reduce the amount of information that videos carry to viewers. I conclude by drawing some implications of this analysis for addressing the epistemic threat of deepfakes. (shrink)

A widespread view among philosophers and scientists is that recorded sounds and assisted hearing differ fundamentally from natural sounds and direct hearing. It is commonly claimed, for example, that the sounds we hear over the phone are not sounds emitted by the voice of our interlocutor, but the sounds reproduced by the phone’s loudspeaker. According to this view, hearing distant sounds through communication and audio equipment is at best indirect and at worst illusory. In what follows, I shall reject these (...) claims and argue in favor of a transparent view of auditory media, including radio, telephone, phonograph, etc. According to this approach, the great gift of Scott de Martinville and Edison is not to have invented devices able to reproduce vanished sounds but rather to have created technological instruments literally able to store and transmit them to future and distant listeners. (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)

Deep learning is currently the most prominent and widely successful method in artificial intelligence. Despite having played an active role in earlier artificial intelligence and neural network research, philosophers have been largely silent on this technology so far. This is remarkable, given that deep learning neural networks have blown past predicted upper limits on artificial intelligence performance—recognizing complex objects in natural photographs and defeating world champions in strategy games as complex as Go and chess—yet there remains no universally accepted explanation (...) as to why they work so well. This article provides an introduction to these networks as well as an opinionated guidebook on the philosophical significance of their structure and achievements. It argues that deep learning neural networks differ importantly in their structure and mathematical properties from the shallower neural networks that were the subject of so much philosophical reflection in the 1980s and 1990s. The article then explores several different explanations for their success and ends by proposing three areas of inquiry that would benefit from future engagement by philosophers of mind and science. (shrink)

Deepfake technology uses machine learning to fabricate video and audio recordings that represent people doing and saying things they've never done. In coming years, malicious actors will likely use this technology in attempts to manipulate public discourse. This paper prepares for that danger by explicating the unappreciated way in which recordings have so far provided an epistemic backstop to our testimonial practices. Our reasonable trust in the testimony of others depends, to a surprising extent, on the regulative effects of the (...) ever-present possibility of recordings of the events they testify about. As deepfakes erode the epistemic value of recordings, we may then face an even more consequential challenge to the reliability of our testimonial practices themselves. (shrink)

AI, especially in the case of Deepfakes, has the capacity to undermine our confidence in the original, genuine, authentic nature of what we see and hear. And yet digital technologies, in the form of databases and other detection tools also make it easier to spot forgeries and to establish the authenticity of a work. Using the notion of ectypes, this paper discusses current conceptions of authenticity and reproduction and examines how, in the future, these might be adapted for use in (...) the digital sphere. (shrink)