The Social Impact of Deepfakes

Can deepfakes manipulate us? Assessing the evidence via a critical scoping review

Deepfakes are one of the most recent developments in misinformation technology and are capable of superimposing one person's face onto another in video format. The potential of this technology to defame and cause harm is clear. However, despite the grave concerns expressed about deepfakes, these concerns are rarely accompanied with empirical evidence. We present a scoping review of the existing empirical studies that aim to investigate the effects of viewing deepfakes on people's beliefs, memories, and behaviour. Five databases were searched, producing an initial sample of 2004 papers, from which 22 relevant papers were identified, varying in methodology and research methods used. Overall, we found that the early studies on this topic have often produced inconclusive findings regarding the existence of uniquely persuasive or convincing effects of deepfake exposure. Moreover, many experiments demonstrated poor methodology and did not include a non-deepfake comparator (e.g., text-based misinformation). We conclude that speculation and scare mongering about dystopian uses of deepfake technologies has far outpaced experimental research that assess these harms. We close by offering insights on how to conduct improved empirical work in this area.

The public mental representations of deepfake technology: An in-depth qualitative exploration through Quora text data analysis

The advent of deepfake technology has raised significant concerns regarding its impact on individuals' cognitive processes and beliefs, considering the pervasive relationships between technology and human cognition. This study delves into the psychological literature surrounding deepfakes, focusing on people's public representation of this emerging technology and highlighting prevailing themes, opinions, and emotions. Under the media framing, the theoretical framework is crucial in shaping individuals' cognitive schemas regarding technology. A qualitative method has been applied to unveil patterns, correlations, and recurring themes of beliefs about the main topic, deepfake, discussed on the forum Quora. The final extracted text corpus consisted of 166 answers to 17 questions. Analysis results highlighted the 20 most prevalent critical lemmas, and deepfake was the main one. Moreover, co-occurrence analysis identified words frequently appearing with the lemma deepfake, including video, create, and artificial intelligence-finally, thematic analysis identified eight main themes within the deepfake corpus. Cognitive processes rely on critical thinking skills in detecting anomalies in fake videos or discerning between the negative and positive impacts of deepfakes from an ethical point of view. Moreover, people adapt their beliefs and mental schemas concerning the representation of technology. Future studies should explore the role of media literacy in helping individuals to identify deepfake content since people may not be familiar with the concept of deepfakes or may not fully understand the negative or positive implications. Increased awareness and understanding of technology can empower individuals to evaluate critically the media related to Artificial Intelligence.

Possible Health Benefits and Risks of DeepFake Videos: A Qualitative Study in Nursing Students

BACKGROUND

"DeepFakes" are synthetic performances created by AI, using neural networks to exchange faces in images and modify voices.

OBJECTIVE

Due to the novelty and limited literature on its risks/benefits, this paper aims to determine how young nursing students perceive DeepFake technology, its ethical implications, and its potential benefits in nursing.

METHODS

This qualitative study used thematic content analysis (the Braun and Clarke method) with videos recorded by 50 third-year nursing students, who answered three questions about DeepFake technology. The data were analyzed using ATLAS.ti (version 22), and the project was approved by the Ethics Committee (code UCV/2021-2022/116).

RESULTS

Data analysis identified 21 descriptive codes, classified into four main themes: advantages, disadvantages, health applications, and ethical dilemmas. Benefits noted by students include use in diagnosis, patient accompaniment, training, and learning. Perceived risks include cyberbullying, loss of identity, and negative psychological impacts from unreal memories.

CONCLUSIONS

Nursing students see both pros and cons in DeepFake technology and are aware of the ethical dilemmas it poses. They also identified promising healthcare applications that could enhance nurses' leadership in digital health, stressing the importance of regulation and education to fully leverage its potential.

Human detection of political speech deepfakes across transcripts, audio, and video

Recent advances in technology for hyper-realistic visual and audio effects provoke the concern that deepfake videos of political speeches will soon be indistinguishable from authentic video. We conduct 5 pre-registered randomized experiments with N = 2215 participants to evaluate how accurately humans distinguish real political speeches from fabrications across base rates of misinformation, audio sources, question framings with and without priming, and media modalities. We do not find base rates of misinformation have statistically significant effects on discernment. We find deepfakes with audio produced by the state-of-the-art text-to-speech algorithms are harder to discern than the same deepfakes with voice actor audio. Moreover across all experiments and question framings, we find audio and visual information enables more accurate discernment than text alone: human discernment relies more on how something is said, the audio-visual cues, than what is said, the speech content.

Cortical-striatal brain network distinguishes deepfake from real speaker identity

Deepfakes are viral ingredients of digital environments, and they can trick human cognition into misperceiving the fake as real. Here, we test the neurocognitive sensitivity of 25 participants to accept or reject person identities as recreated in audio deepfakes. We generate high-quality voice identity clones from natural speakers by using advanced deepfake technologies. During an identity matching task, participants show intermediate performance with deepfake voices, indicating levels of deception and resistance to deepfake identity spoofing. On the brain level, univariate and multivariate analyses consistently reveal a central cortico-striatal network that decoded the vocal acoustic pattern and deepfake-level (auditory cortex), as well as natural speaker identities (nucleus accumbens), which are valued for their social relevance. This network is embedded in a broader neural identity and object recognition network. Humans can thus be partly tricked by deepfakes, but the neurocognitive mechanisms identified during deepfake processing open windows for strengthening human resilience to fake information.

Deepfake forensics: a survey of digital forensic methods for multimodal deepfake identification on social media

The rapid advancement of deepfake technology poses an escalating threat of misinformation and fraud enabled by manipulated media. Despite the risks, a comprehensive understanding of deepfake detection techniques has not materialized. This research tackles this knowledge gap by providing an up-to-date systematic survey of the digital forensic methods used to detect deepfakes. A rigorous methodology is followed, consolidating findings from recent publications on deepfake detection innovation. Prevalent datasets that underpin new techniques are analyzed. The effectiveness and limitations of established and emerging detection approaches across modalities including image, video, text and audio are evaluated. Insights into real-world performance are shared through case studies of high-profile deepfake incidents. Current research limitations around aspects like cross-modality detection are highlighted to inform future work. This timely survey furnishes researchers, practitioners and policymakers with a holistic overview of the state-of-the-art in deepfake detection. It concludes that continuous innovation is imperative to counter the rapidly evolving technological landscape enabling deepfakes.

A face of one's own: The role of an online personae in a digital age and the right to control one's own online personae in the presence of digital hacking

In the post-Covid world, our online personae have become increasingly essential mechanisms for presenting ourselves to the world. Simultaneously, new techniques for hacking online personae have become more widely available, easier to use, and more convincing. This combination, of greater reliance on online personae and easier malicious hacking, has created serious societal problems. Techniques for training users to detect false content have proved ineffective. Unfortunately, legal remedies for dealing with hacked personae have also been inadequate. Consequently, the only remaining alternative is to limit the posting of false content. In this discussion paper, we provide an overview of online personae hacking. As potential remedies, we propose to redesign search engine and social media algorithms allowing platforms to detect and restrict harmful false content and a new fundamental right for the EU Charter that would provide legal justification for platforms to protect online reputations. For those platforms that might choose not to protect online reputations, this new right would require that they do so.

When Potential Allies and Targets Do (and Do Not) Confront Anti-Asian Prejudice: Reactions to Blatant and Subtle Prejudice During the COVID-19 Pandemic

Anti-Asian xenophobia has exploded during the COVID-19 pandemic, after U.S. political leaders promoted anti-Asian rhetoric from its start. Confronting prejudice interrupts future perpetration of such prejudice, but confronting prejudice can only occur to the extent actions are first attributed to prejudice. Bystanders may attribute less prejudice to speech about the "Chinese Virus" than to more blatant stereotype expression, for example, and therefore be less vehement in their confrontations. Across two studies, we examined the impact of anti-Asian prejudice type (blatant, subtle, or no prejudice) and bystander race/ethnicity (White or Asian American/Pacific Islander [AAPI]), on prejudice attribution, willingness to confront, actual confrontation, and confrontation vehemence. In the context of a hiring manager justifying rejection of a Chinese applicant, we predicted that blatant prejudice would be detected and confronted most willingly, and subtle prejudice more willingly than no prejudice, and that prejudice detection would mediate the relationship between prejudice type and willingness to confront. Further, we expected AAPI bystanders to detect anti-Asian prejudice more readily than White bystanders, but to confront at lower rates, with actual confrontations being more vehement following blatant (relative to subtle or no) prejudice. Analyses were conducted using SPSS 27 and the PROCESS v4.1 macro, controlling for potential confounds such as political orientation and individual-level prejudice (expressed or perceived). Results of both studies (n = 142 [Study 1], n = 274 [Study 2]) supported hypotheses, except in Study 1 bystanders exposed to subtle prejudice were no more willing to confront than no-prejudice controls. Results of exploratory analyses indicated that attribution to prejudice was the primary obstacle to confrontation following subtle prejudice, whereas action taking was the primary obstacle following blatant prejudice. This research underscores the need for interventions to increase detection of all forms of anti-Asian prejudice and to provide would-be confronters with effective confrontation tools.

Effects of Using Artificial Intelligence on Interpersonal Perceptions of Job Applicants

Text-based artificial intelligence (AI) systems are increasingly integrated into a host of interpersonal domains. Although decision-making and person perception in hiring and employment opportunities have been an area of psychological interest for many years, only recently have scholars begun to investigate the role that AI plays in this context. To better understand the impact of AI in employment-related contexts, we conducted two experiments investigating how the use of AI by applicants influences their job opportunities. In our preregistered Study 1, we examined how a prospective job applicants' use of AI, as well as their language status (native English speaker or non-native English speaker), influenced participants' impressions of their warmth, competence, social attractiveness, and hiring desirability. In Study 2, we examined how receiving assistance impacted interpersonal perceptions, and how perceptions might change whether the help was provided by AI or by another human. The results from both experiments suggest that the use of AI technologies can negatively influence perceptions of jobseekers. This negative impact may be grounded in the perception of receiving any type of help, whether it be from a machine or a person. These studies provide additional evidence for the Computers as Social Actors framework and advance our understanding of AI-Mediated Communication. The results also raise questions about transparency and deception related to AI use in interpersonal contexts.

Fooled twice: People cannot detect deepfakes but think they can

Hyper-realistic manipulations of audio-visual content, i.e., deepfakes, present new challenges for establishing the veracity of online content. Research on the human impact of deepfakes remains sparse. In a pre-registered behavioral experiment (N = 210), we show that (1) people cannot reliably detect deepfakes and (2) neither raising awareness nor introducing financial incentives improves their detection accuracy. Zeroing in on the underlying cognitive processes, we find that (3) people are biased toward mistaking deepfakes as authentic videos (rather than vice versa) and (4) they overestimate their own detection abilities. Together, these results suggest that people adopt a “seeing-is-believing” heuristic for deepfake detection while being overconfident in their (low) detection abilities. The combination renders people particularly susceptible to be influenced by deepfake content.

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People cannot reliably detect deepfakes

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Raising awareness and financial incentives do not improve people's detection accuracy

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People tend to mistake deepfakes as authentic videos (rather than vice versa)

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People overestimate their own detection deepfake abilities