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)

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 are AI-generated media. When produced competently, they are near-indistinguishable from genuine recordings and so 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)

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)

We investigate the question of whether (and if so why) creating or distributing deepfake pornography of someone without their consent is inherently objectionable. We argue that nonconsensually distributing deepfake pornography of a living person on the internet is inherently pro tanto wrong in virtue of the fact that nonconsensually distributing intentionally non-veridical representations about someone violates their right that their social identity not be tampered with, a right which is grounded in their interest in being able to exercise autonomy over (...) their social relations with others. We go on to suggest that nonconsensual deepfakes are especially worrisome in connection with this right because they have a high degree of phenomenal immediacy, a property which corresponds inversely to the ease with which a representation can be doubted. We then suggest that nonconsensually creating and privately consuming deepfake pornography is worrisome but may not be inherently pro tanto wrong. Finally, we discuss the special issue of whether nonconsensually distributing deepfake pornography of a deceased person is inherently objectionable. We argue that the answer depends on how long it has been since the person died. (shrink)

Deepfakes are fake recordings generated by machine learning algorithms. Various philosophical explanations have been proposed to account for their epistemic harmfulness. In this paper, I argue that deepfakes are epistemically harmful because they undermine trust in recording technology. As a result, we are no longer entitled to our default doxastic attitude of believing that P on the basis of a recording that supports the truth of P. Distrust engendered by deepfakes changes the epistemic status of recordings to (...) resemble that of handmade images. Their credibility, like that of testimony, depends partly on the credibility of the source. I consider some proposed technical solutions from a philosophical perspective to show the practical relevance of these suggestions. (shrink)

The recent emergence of machine-manipulated media raises an important societal question: How can we know whether a video that we watch is real or fake? In two online studies with 15,016 participants, we present authentic videos and deepfakes and ask participants to identify which is which. We compare the performance of ordinary human observers with the leading computer vision deepfake detection model and find them similarly accurate, while making different kinds of mistakes. Together, participants with access to the model’s (...) prediction are more accurate than either alone, but inaccurate model predictions often decrease participants’ accuracy. To probe the relative strengths and weaknesses of humans and machines as detectors of deepfakes, we examine human and machine performance across video-level features, and we evaluate the impact of preregistered randomized interventions on deepfake detection. We find that manipulations designed to disrupt visual processing of faces hinder human participants’ performance while mostly not affecting the model’s performance, suggesting a role for specialized cognitive capacities in explaining human deepfake detection performance. -/- . (shrink)

The recent proliferation of deepfakes and other digitally produced deceptive representations has revived the debate on the epistemic robustness of photography and other mechanically produced images. Authors such as Rini (2020) and Fallis (2021) claim that the proliferation of deepfakes pose a serious threat to the reliability and the epistemic value of photographs and videos. In particular, Fallis adopts a Skyrmsian account of how signals carry information (Skyrms, 2010) to argue that the existence of deepfakes significantly reduces (...) the information that images carry about the world, which undermines their reliability as a source of evidence. In this paper, we focus on Fallis’ version of the challenge, but our results can be generalized to address similar pessimistic views such as Rini’s. More generally, we offer an account of the epistemic robustness of photography and videos that allows us to understand these systems of representation as continuous with other means of information transmission we find in nature. This account will then give us the necessary tools to put Fallis’ claims into perspective: using a richer approach to animal signaling based on the signaling model of communication (Maynard-Smith and Harper, 2003), we will claim that, while it might be true that deepfake technology increases the probability of obtaining false positives, the dimension of the epistemic threat involved might still be negligible. (shrink)

Deepfakes are algorithmically modified video and audio recordings that project one person’s appearance on to that of another, creating an apparent recording of an event that never took place. Many scholars and journalists have begun attending to the political risks of deepfake deception. Here we investigate other ways in which deepfakes have the potential to cause deeper harms than have been appreciated. First, we consider a form of objectification that occurs in deepfaked ‘frankenporn’ that digitally fuses the parts (...) of different women to create pliable characters incapable of giving consent to their depiction. Next, we develop the idea of ‘illocutionary wronging’, in which an individual is forced to engage in speech acts they would prefer to avoid in order to deny or correct the misleading evidence of a publicized deepfake. Finally, we consider the risk that deepfakes may facilitate campaigns of ‘panoptic gaslighting’, where many systematically altered recordings of a single person's life undermine their memory, eroding their sense of self and ability to engage with others. Taken together, these harms illustrate the roles that social epistemology and technological vulnerabilities play in human ethical life. (shrink)

Deepfakes are becoming increasingly sophisticated and pervasive in society. While deepfakes are often associated with negative applications and risks (e.g., threats to privacy and security), more positive applications are also being explored, like the potential benefits that deepfakes could have for psychotherapy (e.g., to cope with grief or process trauma). To date, there has been insufficient discussion about the philosophical and ethical issues raised of these developments. In this paper, we therefore examine four ethical issues raised by (...) the use of deepfakes for psychotherapy: (1) the deceptive nature of deepfakes, including self-deception, (2) the problem of obtaining consent from depicted persons, (3) treating depicted persons as mere means, and (4) the risks of unintended harmful effects, like increased emotional and psychological dependence on deepfakes. We also reflect on larger potential implications for society and human care practices. We finally offer two concrete recommendations, namely that more empirical research should be conducted to determine the effects of using deepfakes for psychotherapy, and that regulatory oversight is needed if deepfakes are to become more widely adopted as therapeutic tools in the future. (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)

Deepfake technology, powered by artificial intelligence and deep learning, has rapidly advanced, enabling the creation of highly realistic synthetic media. While it presents opportunities in entertainment and creative applications, deepfakes pose significant risks, including misinformation, identity fraud, and threats to privacy and national security. This study explores the evolution of deepfake technology, its implications, and current detection techniques. Existing methods for deepfake detection, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), are examined, (...) highlighting their effectiveness and limitations. The study also reviews state-of-the-art approaches in image forensics, phoneme-viseme mismatch detection, and adversarial training to counter deepfake threats. Moreover, the ethical and legal challenges surrounding deepfakes are discussed, emphasizing the need for policy regulations and collaborative efforts between governments, tech companies, and researchers. As deepfake technology continues to evolve, so must detection strategies, integrating multimodal analysis and real-time verification systems. This research underscores the importance of developing robust detection frameworks and public awareness initiatives to mitigate the risks associated with deepfakes. Future directions include enhancing detection algorithms through explainable AI, improving dataset quality, and integrating blockchain for digital content authentication. By providing a comprehensive analysis of deepfake creation, detection, and countermeasures, this study contributes to the ongoing discourse on synthetic media and its societal impact. Addressing these challenges requires interdisciplinary collaboration and continuous innovation to safeguard digital integrity and trust in the information ecosystem. (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)

[Author note: There is a video explainer of this paper on youtube at the new work in philosophy channel (search for surname+deepfakes).] -/- It is widely thought that deepfake videos are a significant and unprecedented threat to our epistemic practices. In some writing about deepfakes, manipulated videos appear as the harbingers of an unprecedented _epistemic apocalypse_. In this paper I want to take a critical look at some of the more catastrophic predictions about deepfake videos. I will argue (...) for three claims: (1) that once we recognise the role of social norms in the epistemology of recordings, deepfakes are much less concerning, (2) that the history of photographic manipulation reveals some important precedents, correcting claims about the novelty of deepfakes, and (3) that proposed solutions to deepfakes have been overly focused on technological interventions. My overall goal is not so much to argue that deepfakes are not a problem, but to argue that behind concerns around deepfakes lie a more general class of social problems about the organisation of our epistemic practices. (shrink)

Deepfake technology can be used to produce videos of real individuals, saying and doing things that they never in fact said or did, that appear highly authentic. Having accepted the premise that Deepfake content can constitute a legitimate form of expression, it is not immediately clear where the rights of content producers and distributors end, and where the rights of individuals whose likenesses are used in this content begin. This paper explores the question of whether it can be plausibly argued (...) that Deepfake content involving the likenesses of real individuals violates the rights of these individuals. (shrink)

This paper takes up the question of how videographic public announcements (VPAs)---i.e. videos that a wide swath of the public sees and knows that everyone else can see too--- have functioned to mobilize people politically, and how the presence of deepfakes in our information environment stands to change the dynamics of this mobilization. Existing work by Regina Rini, Don Fallis and others has focused on the ways that deepfakes might interrupt our acquisition of first-order knowledge through videos. But (...) I point out that even where every audience member takes a video to be veridical, where first-order knowledge acquisition is secure, an audience aware of deepfakes in their environment will not acquire higher order knowledge in the way that has erstwhile been characteristic for audiences of VPAs. Engaging with ideas from the literatures on public announcement logic, common knowledge, and convention, I enumerate a variety of ways in which we should expect this absence of higher order knowledge to throw up barriers to political mobilization. I go on to apply my analysis of VPAs to the mechanisms by which an uptick in publicly available videos of police brutality over the last decade, mediated by camera phones and social media, was responsible for the largest mass protests in US history in summer 2020. This makes vivid the stakes of the transformation in our mobilizing environment that I've claimed deepfakes effect: where we lose the common-knowledge-generating effects of VPAs, there are fresh obstacles to this sort of mass mobilization. (shrink)

Recent develops in AI technology have led to increasingly sophisticated forms of video manipulation. One such form has been the advent of deepfakes. Deepfakes are AI-generated videos that typically depict people doing and saying things they never did. In this paper, I demonstrate that there is a close structural relationship between deepfakes and more traditional fake barn cases in epistemology. Specifically, I argue that deepfakes generate an analogous degree of epistemic risk to that which is found (...) in traditional cases. Given that barn cases have posed a long-standing challenge for virtue-theoretic accounts of knowledge, I consider whether a similar challenge extends to deepfakes. In doing so, I consider how Duncan Pritchard’s recent anti-risk virtue epistemology meets the challenge. While Pritchard’s account avoids problems in traditional barn cases, I claim that it leads to local scepticism about knowledge from online videos in the case of deepfakes. I end by considering how two alternative virtue-theoretic approaches might vindicate our epistemic dependence on videos in an increasingly digital world. (shrink)

In recent years, a number of philosophers have turned their attention to developments in Artificial Intelligence, and in particular to deepfakes. A deepfake is a portmanteau of ‘deep learning' and ‘fake', and for the most part they are videos which depict people doing and saying things they never did. As a result, much of the emerging literature on deepfakes has turned on questions of trust, harms, and information-sharing. In this paper, I add to the emerging concerns around (...) class='Hi'>deepfakes by drawing on resources from vice epistemology. As deepfakes become more sophisticated, I claim, they will develop to be a source of online epistemic corruption. More specifically, they will encourage consumers of digital online media to cultivate and manifest various epistemic vices. My immediate focus in this paper is on their propensity to encourage the development of what I call ‘intellectual cynicism'. After sketching a rough account of this epistemic vice, I go on to suggest that we can partially offset such cynicism – and fears around deceptive online media more generally – by encouraging the development what I term a trained ‘digital sensibility'. This, I contend, involves a calibrated sensitivity to the epistemic merits of online content. (shrink)

Could 2024 be the year heralding what one could term the deepfake universe apocalypse scenario or could it be the year that a future history of science may e.g. interpret as the year of the first literally universe-sized algorithmic hype bubble? This commentary introduces the metaphor of "GPT-Universe" and the assumptions hidden beneath it.

Would suppressing deepfakes violate freedom of expression norms? The question is pressing because the deepfake phenomenon in its more poisonous manifestations appears to call for a response, and automated targeting of some kind looks to be the most practically viable. Two simple answers are rejected: that deepfakes do not deserve protection under freedom of expression legislation because they are fake by definition; and that deepfakes can be targeted if but only if they are misleadingly presented as authentic. (...) To make progress, following a discussion of why freedom of expression deserves protection in a democracy, the question is reframed. At issue is not whether the arrival of deepfakes brings new and potentially serious dangers (it does), nor whether these dangers call for measures that potentially limit freedom of expression (they do), but whether the need for such measures raises any new and unfamiliar freedom-of-expression challenges. The answer to that question, surprisingly, is no. The balancing act needed to cope with the arrival of deepfakes brings plenty of difficulties, certainly, but none of the measures likely to be effective in tackling deepfake harms raises freedom-of-expression concerns that aren’t familiar from consideration of non-deepfake harms. In that respect, at least, the arrival of deepfakes makes no difference. (shrink)

In this paper, I present an analysis of the depictive properties of deepfakes. These are videos and pictures produced by deep learning algorithms that automatically modify existing videos and photographs or generate new ones. I argue that deepfakes have an intentional standard of correctness. That is, a deepfake depicts its subject only insofar as its creator intends it to. This is due to the way in which these images are produced, which involves a degree of intentional control similar (...) to that involved in the production of other intentional pictures such as drawings and paintings. This aspect distinguishes deepfakes from real videos and photographs, which instead have a non-intentional standard: their correct interpretation corresponds to the scenes that were recorded by the mechanisms that produced them, and not to what their producers intended them to represent. I show that these depictive properties make deepfakes fit for communicating information in the same way as language and other intentional pictures. That is, they do not provide direct access to the communicated information like non-intentional pictures (e.g., videos and photographs) do. Rather, they convey information indirectly, relying only on the viewer's assumptions about the communicative intentions of the creator of the deepfake. Indirect communication is indeed a prominent function of such media in our society, but it is often overlooked in the philosophical literature. This analysis also explains what is epistemically worrying about deepfakes. With the introduction of this technology, viewers interpreting photorealistic videos and pictures can no longer be sure of which standard to select (i.e., intentional or non-intentional), which makes misinterpretation likely (i.e., they can take an image to have a standard of correctness that it does not have). (shrink)

So-called “deepfakes” (i.e., highly believable but fabricated media) are infamous for their potential political application. However, they can also contain false information about individuals, which raises the question whether deepfakes can violate a moral right to privacy. This question is directly related to the often ignored but still highly contentious issue of whether the spreading or use of false or fake information can violate a moral right to privacy. While such queries normally turn on how we conceptualize a (...) moral right to privacy, they are answered here based on premises that any conception of the moral right to privacy must adhere to. (shrink)

This paper defends two main theses related to emerging deepfake technology. First, fears that deepfakes will bring about epistemic catastrophe are overblown. Such concerns underappreciate that the evidential power of video derives not solely from its content, but also from its source. An audience may find even the most realistic video evidence unconvincing when it is delivered by a dubious source. At the same time, an audience may find even weak video evidence compelling so long as it is delivered (...) by a trusted source. The growing prominence of deepfake content is unlikely to change this fundamental dynamic. Thus, through appropriate patterns of trust, whatever epistemic threat deepfakes pose can be substantially mitigated. The second main thesis is that focusing on deepfakes that are intended to deceive, as epistemological discussions of the technology tend to do, threatens to overlook a further effect of this technology. Even where deepfake content is not regarded by its audience as veridical, it may cause its viewers to develop psychological associations based on that content. These associations, even without rising to the level of belief, may be harmful to the individuals depicted and more generally. Moreover, these associations may develop in cases in which the video content is realistic, but the audience is dubious of the content in virtue of skepticism toward its source. Thus, even if—as I suggest—epistemological concerns about deepfakes are overblown, deepfakes may nonetheless be psychologically impactful and may do great harm. (shrink)

Sexualized deepfakes are frequently framed as a problem of deception, misinformation, or privacy. However, a growing body of scholarship has shown that the central harm of deepfake pornography does not primarily lie in viewers being deceived, but in the non-consensual creation and circulation of sexualized representations. Building on this insight, this article examines sexualized deepfakes as socio-technical mechanisms of gendered power. Drawing on science and technology studies and feminist media theory, it argues that deepfake pornography extends existing practices (...) of image-based sexual abuse by weaponizing visual representation against women. The paper situates sexualized deepfakes within longer histories of visual domination and analyzes how digital platforms, AI infrastructures, and cultural norms amplify these harms. It shows that current governance approaches, including consent-based legal frameworks, victim-centered resilience strategies, and technical detection tools, address only partial dimensions of a fundamentally systemic problem. In response, the article proposes a consent-centered socio-technical framework for governing deepfake abuse that integrates platform accountability, legal reform, technical safeguards, and victim support. Understanding sexualized deepfakes as instruments of power rather than merely falsified media shifts the focus of governance from protecting authenticity toward protecting autonomy, dignity, and justice in the age of generative AI. (shrink)

Deepfakes are synthetic media that superimpose or generate someone’s likeness onto 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. (shrink)

The illicit diffusion of intimate photographs or videos intended for private use is a troubling phenomenon known as the diffusion of Non-Consensual Intimate Images (NCII). Recently, it has been feared that the spread of deepfake technology, which allows users to fabricate fake intimate images or videos that are indistinguishable from genuine ones, may dramatically extend the scope of NCII. In the present essay, we counter this pessimistic view, arguing for qualified optimism instead. We hypothesize that the growing diffusion of (...) class='Hi'>deepfakes will end up disrupting the status that makes our visual experience of photographic images and videos epistemically and affectively special; and that once divested of this status, NCII will lose much of their allure in the eye of the perpetrators, probably resulting in diminished diffusion. We conclude by offering some caveats and drawing some implications to better understand, and ultimately better counter, this phenomenon. (shrink)

Deepfake technology facilitates the alteration of a person’s facial features in videos or images, frequently employed for non-consensual pornographic material disseminated on social media platforms. Individuals affected by deepfake pornography in Indonesia encounter significant obstacles in asserting their right to be forgotten, as specified in the Law on Sexual Violence Crimes. These challenges stem from a lack of implementing regulations, inadequate legal frameworks, and law enforcement practices that do not adequately consider gender issues. The absence of regulatory frameworks and the (...) prevalent misuse of AI technology have resulted in a notable increase in incidents, with platforms such as Telegram emerging as key areas for disseminating altered content. Victims experience significant psychological, social, and economic repercussions. Although organizations such as the National Commission on Violence Against Women assist, there are still legal barriers that persist. In order to safeguard victims and implement the Right to Be Forgotten, Indonesia needs to tackle various legal, cultural, and enforcement obstacles, especially the insufficient gender sensitivity within law enforcement and the ambiguity surrounding relevant laws. A joint initiative involving both government and society is essential to protect victims' rights and address the increasing prevalence of deepfake pornography. (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)

Audio and video footage produced with the help of AI can show politicians doing discreditable things that they have not actually done. This is deepfaked material. Deepfakes are sometimes claimed to have special powers to harm the people depicted and their audiences—powers that more traditional forms of faked imagery and sound footage lack. According to some philosophers, deepfakes are particularly “believable,” and widely available technology will soon make deepfakes proliferate. I first give reasons why deepfake technology is (...) not particularly well suited to producing “believable” political misinformation in a sense to be defined. Next, I challenge claims from Don Fallis and Regina Rini about the consequences of the wide availability of deepfakes. My argument is not that deepfakes are harmless, but that their power to do major harm is highly conditional in liberal party political environments that contain sophisticated mass-media. (shrink)

While the concerns about media trust have historical roots, the dynamics have evolved with technological advancements, the rise of digital media, and the challenges posed by disinformation in the 21st century. Deepfake – the media content (text, images, videos) created by AI technology – is just another concern for journalists, politicians and society in the current media trust crisis. It is a tool that can be successfully applied in an information warfare. To be honest, face and voice spoofing has always (...) existed, but deepfake technology may now make fake videos faster, cheaper, with high quality and is accessible to anybody. Although deepfake videos have the potential to undermine trust in journalism, their actual impact on audiences remains limited due to information overload, increased suspicion and skeptical attitude, decreased trust in media and advanced audience adaptability for orientation in the information flow. (shrink)

Non-Consensual Sexual Deepfakes (NCSD) are a growing issue of our onlife experience. We argue that, far from harming individuals merely indirectly, NCSD constitute a _Direct Personal Harm_ to the persons they depict, because they can be understood as parts of their personal identity. We defend this claim by developing a narrative theory of identity according to which a person is constituted by their life story—a story that is not solely self-authored but socially shaped and often beyond one’s control. On (...) this basis, we argue that digital artifacts such as NCSD can become elements of a person’s narrative identity. We ground our account in a four-dimensionalist metaphysics that treats persons as aggregates of both biological and digital temporal parts. Finally, by examining the verbal behavior of imaginary and actual victims of NCSD, we show that this account is less counterintuitive than it may initially appear and helps make sense of why many experience NCSD as violations of their person, rather than merely of their representation. (shrink)

Purpose This study aims to investigate the ethical implications of deepfake technologies and their influence on public trust in digital content. This research empirically examines perceptions among social media users in India – a context marked by high internet penetration but uneven digital literacy – while investigating the ethical implications of deepfake technologies and their influence on public trust in digital content. As synthetic media becomes increasingly indistinguishable from authentic material, concerns related to consent, identity manipulation, misinformation and information integrity (...) have intensified. The study also aims to empirically assess user perceptions of these risks and explore the relationship between ethical concerns, trust in digital platforms and expectations for regulation. Design/methodology/approach A cross-sectional survey was conducted among 412 digital media users in India. Grounded in Floridi’s Information Ethics, Digital Trust Theory and the framework of Responsible Innovation, the study used descriptive statistics, correlation analysis, multiple regression and additional validation tests (including Cronbach’s alpha, variance inflation factor analysis and Harman’s single-factor test) to examine relationships between exposure to deepfakes, ethical concern, trust in digital content and expectations for regulation. Findings The findings reveal that ethical concern significantly predicts digital trust, while exposure and confidence in identifying deepfakes do not. Participants expressed strong support for regulatory oversight and platform accountability, underscoring the public’s demand for ethical governance in synthetic media. Reliability analysis confirmed the internal consistency of measurement scales, and no common method bias or multicollinearity was detected. Originality/value This paper offers a unique empirical contribution to the literature on information ethics, digital trust and artificial intelligence governance by integrating ethical theory with quantitative insights from a large sample in an emerging economy. It provides actionable implications for policymakers, platform designers and educators aiming to enhance digital integrity in the age of synthetic media. (shrink)

Most scholars think that using deepfakes to cause epistemic harm (i.e., to mislead, conceal truth, etc.) may cause us to stop trusting digital testimonies as a whole. This, in turn, will have serious consequences on our interpersonal relations—since most of our communication is conducted in the digital world. I argue that deepfakes created for non-malevolent purposes (e.g., for fun) are epistemically far more dangerous: they undermine digital communication inadvertently and to a much greater extent than malevolent deepfakes. (...) Specifically, they allow malevolent deepfakes to camouflage themselves as non-malevolent (i.e., blend with the environment) thus creating a context of epistemic uncertainty. However, we may have reasons to believe that rational self-interested agents will agree to substantially limit or ban the use of deepfakes. Rather than educating people on the nature of deepfakes or misinformation, we should educate them on the potential harm they could cause to others and themselves by creating and disseminating deepfakes. (shrink)

Deepfakes are a new form of synthetic media that broke upon the world in 2017. Bringing photoshopping to video, deepfakes replace people in existing videos with someone else’s likeness. Currently most of their reach is limited to pornography, and they are also used to discredit people. However, deepfake technology has many epistemic promises and perils, which concern how we fare as knowers. Our goal is to help set an agenda around these matters, to make sure this technology can (...) help realize epistemic rights and epistemic justice and unleash human creativity, rather than inflict epistemic wrongs of any sort. Our project is exploratory in nature, and we do not aim to offer conclusive answers at this early stage. There is a need to remain vigilant to make sure the downsides do not outweigh the upsides, and that will be a tall order. (shrink)

Videos have suddenly become a common affair in the present day which has gained prominence all of a sudden Interestingly and sadly many well known celebrities have been caught in this fiasco. During the last general elections in India, the Home Minister’s speech was edited and was circulated in all social media platforms which created a furore among the netizens especially who had heard him in one of his rallies. In the same way, there are many instances where high profile (...) celebrities in the movie and media world have been regularly been victimized by the deepfake videos. One such example is the world famous movie star, Priyanka Chopra, who had once admitted that she was a victim of deepfake when she was shown disclosing her annual income in a particular video. “Sometimes it becomes very scary”, as another Bollywood actor, Ms. Rashmika Mandana calls it. Thus we analyze here in this chapter what is deepfake and how dangerous it is for gender violence. We shall also discuss some victims of deepfake and the impact it has caused on them. (shrink)

Deepfakes are synthetic media, such as pictures, music and videos, created with generative artificial intelligence (GenAI) tools, a technique that builds upon machine learning and multi-layered neural networks trained on large datasets. Today, anyone can create deepfakes online, without any knowledge about the underpinning technology. This fact opens up creative opportunities at the same time as it creates individual and societal challenges. Some identified challenges are fake news, bullying, defamation, media manipulation and democracy damage. The aim of this (...) study was to analyse and discuss how deepfakes could affect entertainment, education and digital news media in positive and negative ways. The chosen research strategy was a qualitative cross-sectional study to create a snapshot of current attitudes in blogs, online discussion fora and from a selected expert panel. Firstly, blogs and internet discussion fora on deepfakes and AI were analysed inductively. Secondly, results from the previous step were used to create a question scheme for semi-structured interviews with some selected experts that have relevant knowledge in the field of AI and deepfakes. Finally, interviews answered were analysed thematically using the Technology Affordances and Constraints Theory as an analytical lens. Findings show that deepfakes at the same time have a wide variety of affordances, but also some critical constraints to consider. In the affordance part, there are the potential use of deepfakes for speculative and provoking concepts, not only as entertainment, but also to illustrate educational scenarios that are hard or impossible to capture in traditional videos, images or sound recordings. Furthermore, there are many affordances for entertainment and the creation of environments where realism is not an important condition. Some examples are video game graphics, animated film background music in games and music videos. However, the conclusion is that the findings comprise more of constraints than of affordances, where the serious negative aspect of deepfakes can lead to psychological, financial and social harm. Many images and videos in news media have already been manipulated during the last decades, but deepfakes can take this to a new and more problematic level where the overall trust might disappear. The author’s recommendation is to further increase the focus on source criticism and that discussions on which sources to trust should be an early introduced part of educational programmes. (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 synthetic media, such as pictures, music and videos, created with generative artificial intelligence (GenAI) tools, a technique that builds upon machine learning and multi-layered neural networks trained on large datasets. Today, anyone can create deepfakes online, without any knowledge about the underpinning technology. This fact opens up creative opportunities at the same time as it creates individual and societal challenges. Some identified challenges are fake news, bullying, defamation, media manipulation and democracy damage. The aim of this (...) study was to analyse and discuss how deepfakes could affect entertainment, education and digital news media in positive and negative ways. The chosen research strategy was a qualitative cross-sectional study to create a snapshot of current attitudes in blogs, online discussion fora and from a selected expert panel. Firstly, blogs and internet discussion fora on deepfakes and AI were analysed inductively. Secondly, results from the previous step were used to create a question scheme for semi-structured interviews with some selected experts that have relevant knowledge in the field of AI and deepfakes. Finally, interviews answered were analysed thematically using the Technology Affordances and Constraints Theory as an analytical lens. Findings show that deepfakes at the same time have a wide variety of affordances, but also some critical constraints to consider. In the affordance part, there are the potential use of deepfakes for speculative and provoking concepts, not only as entertainment, but also to illustrate educational scenarios that are hard or impossible to capture in traditional videos, images or sound recordings. Furthermore, there are many affordances for entertainment and the creation of environments where realism is not an important condition. Some examples are video game graphics, animated film background music in games and music videos. However, the conclusion is that the findings comprise more of constraints than of affordances, where the serious negative aspect of deepfakes can lead to psychological, financial and social harm. Many images and videos in news media have already been manipulated during the last decades, but deepfakes can take this to a new and more problematic level where the overall trust might disappear. The author’s recommendation is to further increase the focus on source criticism and that discussions on which sources to trust should be an early introduced part of educational programmes. (shrink)

Deepfakes is a term for content generated by an artificial intelligence (AI) with the intention to be perceived as real. Deepfakes have gained notoriety in their potential misuse in disinformation, propaganda, pornography, defamation, or financial fraud. Despite prominent discussions on the potential harms of deepfakes, empirical evidence on the harms of deepfakes on the human mind remains sparse, wide, and unstructured. This scoping review presents an overview of the research on how deepfakes can negatively affect (...) human mind and behavior. Out of initially 1,143 papers, 28 were included in the scoping review. Several types of harm were identified: Concerns and worries, deception consequences (including false memories, attitude shifts, sharing intention, and false investment choices), mental health harm (including distress, anxiety, reduces self-efficacy, and sexual deepfake victimization), and distrust in media. We conclude that deepfake harm ranges widely and is often hypothetical; hence, empirical investigated of potential harms on human mind and behavior and further methodological refinement to validate current findings is warranted. (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)

Deepfakes, as hyperrealistic digital fabrications, reveal gaps and uncertainties in existing ontological frameworks. Neither simply images nor realities, deepfakes occupy an ambiguous metaphysical position between concepts such as representation/simulation, human/machine, and real/artificial. Their emergent generation via AI and experiential traction as credible synthetic media underscores limitations in prevailing paradigms reliant on purified binaries and anthropocentric assumptions. Rather than anomalies, deepfakes epitomize the imperative for new ontological cartographies and conceptual vocabularies attuned to increasingly unbounded algorithmic creation. The paper (...) surveys debates about digital objects and theories of representation to contextualize this argument. Deepfakes’ ontological novelty stems from their ambiguous origins, autonomous proliferation, and infiltration of perceivable situations with credible fictions. This destabilizes ontological foundations such as creative intentionality and indexical truth. I conclude deepfakes signal the insufficiency of existing ontology for navigating realities transformed by generative technologies. New paradigms are needed that grapple with their radical metaphysical challenges. (shrink)

Concerns about GenAI’s energy use and physical pollution are well known. This commentary highlights another threat: epistemic pollution—the degradation of our knowledge ecosystem through low-quality, misleading, or fabricated information. Drawing on a recent pseudo-scientific article co-authored by Grok 3, we discuss the rise of academic deepfakes and their contribution to epistemic pollution. We show how GenAI makes academic deepfakes cheaper, more accessible, and harder to detect. The resulting epistemic pollution poses acute risks in fields like Business & Society, (...) which draw on multidisciplinary knowledge and engage with politically sensitive topics. Yet, by extending the analogy between epistemic and physical pollution, we suggest that Business & Society scholars are also well placed to study the problem and respond. (shrink)

Deepfakes, as hyperrealistic digital fabrications, reveal gaps and uncertainties in existing ontological frameworks. Neither simply images nor realities, deepfakes occupy an ambiguous metaphysical position between concepts such as representation/simulation, human/machine, and real/artificial. Their emergent generation via AI and experiential traction as credible synthetic media underscores limitations in prevailing paradigms reliant on purified binaries and anthropocentric assumptions. Rather than anomalies, deepfakes epitomize the imperative for new ontological cartographies and conceptual vocabularies attuned to increasingly unbounded algorithmic creation. The paper (...) surveys debates about digital objects and theories of representation to contextualize this argument. Deepfakes’ ontological novelty stems from their ambiguous origins, autonomous proliferation, and infiltration of perceivable situations with credible fictions. This destabilizes ontological foundations such as creative intentionality and indexical truth. I conclude deepfakes signal the insufficiency of existing ontology for navigating realities transformed by generative technologies. New paradigms are needed that grapple with their radical metaphysical challenges. (shrink)

This epistemic project examines an understudied existential risk emerging in the deepfake era: the fortunately up to this time (but not indefinitely so) reversible peril of humanity’s epistemic self-sabotage through an overestimation of algorithms linked to quantitative aspects and a paired underestimation of the own epistemic potential whose manifestations are in principle expressible via scientifically analyzable but currently often neglected qualitative facets. This scenario is metaphorically referred to as "π-Doom scenario". Instead of carefully crafting opaque hypotheses and formulating probabilistic predictions (...) of timelines that could easily evade scrutiny and/or could even themselves be initially generated by algorithms, scientifically and responsibly counteracting π-Doom requires new better riskier explanatory theories of intelligence, creativity and consciousness. A brief non-comprehensive literature review suggests that quite a few scientific instantiations (all of which have not yet been provisionally refuted) of that transformative requirement are already available at present. In short, π-Doom – the voluntarily or unintentionally effectuated cognitive resignation engendering an irrational algorithmically-motored process of running around in (epistemic) circles that could endanger the immediate future of a vulnerable civilization like present-day humanity – is wholly contingent on a choice of focus. (shrink)

Der Begriff der Glaubwürdigkeit begegnet uns heutzutage in vielen verschiedenen Kontexten. Aber wer oder was ist eigentlich glaubwürdig? Was bedeutet es, wenn wir Glaubwürdigkeit zuschreiben? Diese Fragen sollen im folgenden Beitrag im Hinblick auf die Fotografie in ihren verschiedenen Verwendungskontexten näher untersucht werden. Das Problem der Deepfakes hat im digitalen Zeitalter zu einer wahren Glaubwürdigkeitskrise geführt, wie einige Forschende anführen. Was können wir dem Einfluss solcher Bilderzeugnisse entgegensetzen? Hier werden wir auf einen Gedankengang von Catherine Z. Elgin (2025) zurückgreifen, (...) nämlich die Idee einer epistemischen Ökologie. (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 technology allows for the creation of synthetic audio and video content that appears authentic, offering both risks and opportunities for journalism. Such synthetic content may enhance personalization, visualization, and immersion, but challenge the detection of disinformation. To date, there is limited empirical knowledge about the impacts of deepfakes on journalism. Thus, this study’s goal is to explore how journalists assess potential risks and opportunities and how news organizations respond to deepfakes or synthetic content. Interviews with representatives (...) of major Swiss news organizations show that journalists predominantly consider deepfakes as a form of disinformation, and that they have trouble imagining the technology’s potential opportunities for journalism. Moreover, although specialized technological knowledge in news organizations is crucial, mitigating the risks posed by deepfakes and taking advantage of the opportunities synthetic content also require a reorientation toward professional journalistic norms and raising awareness among journalists and audiences alike. These results highlight that deepfakes make it necessary for journalists to renegotiate the role of journalism, professional norms, and the boundaries of AI manipulation. (shrink)

The fast development of synthetic media, commonly known as deepfakes, has cast new light on an old problem, namely—to what extent do people have a moral claim to their likeness, including personally distinguishing features such as their voice or face? That people have at least some such claim seems uncontroversial. In fact, several jurisdictions already combat deepfakes by appealing to a “right to identity.” Yet, an individual’s disapproval of appearing in a piece of synthetic media is sensible only (...) insofar as the replication is successful. There has to be some form of identity between the content and the natural person. The question, therefore, is how this identity can be established. How can we know whether the face or voice featured in a piece of synthetic content belongs to a person who makes claim to it? On a trivial level, this may seem an easy task—the person in the video is A insofar as he or she is recognised as being A. Providing more rigorous criteria, however, poses a serious challenge. In this paper, I draw on Turing’s imitation game, and Floridi’s method of levels of abstraction, to propose a heuristic to this end. I call it the identification game. Using this heuristic, I show that identity cannot be established independently of the purpose of the inquiry. More specifically, I argue that whether a person has a moral claim to content that allegedly uses their identity depends on the type of harm under consideration. (shrink)