Whose dystopia is it anyway? Deepfakes and social media regulation

Yadlin-Segal, Aya; Oppenheim, Yael L.

doi:10.1177/1354856520923963

Cited by 49 publications

(19 citation statements)

References 31 publications

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“…However, I also want to acknowledge that these new digital resources for learning, such as using YouTube to find information, also have pitfalls. For examples, when students are searching online for information there is the potential to encounter misinformation such as Deepfakes (Yadlin-Segal & Oppenheim, 2020). With Deepfakes, audiovisual manipulating artificial intelligence (AI) applications synthesize multiple audiovisual products into one manipulated media, which is usually in the form of a video (Yadlin-Segal & Oppenheim, 2020, p. 2).…”

Section: Discussionmentioning

confidence: 99%

Silly Putty: Mobilization of the Known Across Technology-Enhanced Learning Spaces

Wong

2021

L & L

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In technology-enhanced classrooms, due to the affordances of technologies, English Language Learners (ELLs) are moving between learning spaces; boundaries are never clear. Questions arise with regards to how students’ non-sanctioned experiences might mediate classroom learning. Using a multiliteracies (New London Group, 2000) and learning by design (Cope & Kalantzis, 2015) theoretical lens, this ethnographic case study explores the technology-enhanced learning experiences of Grade 6 ELLs. Data including field observations, artifacts, and interviews were analyzed using thematic analysis (Saldaña, 2016). Findings of this study indicate that ELL students were engaged in learning experiences that encouraged them to mobilize their known (knowledge) across learning spaces.

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Section: Discussionmentioning

confidence: 99%

Silly Putty: Mobilization of the Known Across Technology-Enhanced Learning Spaces

Wong

2021

L & L

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“…Many authors warn of the important repercussions that failure to curb their spread may pose, both to the population (Newman et al, 2015) and to democracy (Bennett & Livingston, 2018;Chadwick, Vaccari, & O'Loughlin, 2018;Rojecki & Meraz, 2016;Waisbord, 2018). There are even those who state that their fast and widespread dissemination can lead to great economic loss or national security risks (Yadlin-Segal & Oppenheim, 2020). In parallel fashion, if deepfakes contribute to increased uncertainty regarding the information they contain, another one of the risks of their use is reduced trust in the news media (Fletcher, 2018;Vaccari & Chadwick, 2020).…”

Section: State Of Playmentioning

confidence: 99%

Deepfakes on Twitter: Which Actors Control Their Spread?

Dasilva

Ayerdi

Galdospín

2021

MaC

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The term deepfake was first used in a Reddit post in 2017 to refer to videos manipulated using artificial intelligence techniques and since then it is becoming easier to create such fake videos. A recent investigation by the cybersecurity company Deeptrace in September 2019 indicated that the number of what is known as fake videos had doubled in the last nine months and that most were pornographic videos used as revenge to harm many women. The report also highlighted the potential of this technology to be used in political campaigns such as in Gabon and Malaysia. In this sense, the phenomenon of deepfake has become a concern for governments because it poses a short-term threat not only to politics, but also for fraud or cyberbullying. The starting point of this research was Twitter’s announcement of a change in its protocols to fight fake news and deepfakes. We have used the Social Network Analysis technique, with visualization as a key component, to analyze the conversation on Twitter about the deepfake phenomenon. NodeXL was used to identify main actors and the network of connections between all these accounts. In addition, the semantic networks of the tweets were analyzed to discover hidden patterns of meaning. The results show that half of the actors who function as bridges in the interactions that shape the network are journalists and media, which is a sign of the concern that this sophisticated form of manipulation generates in this collective.

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“…Journalistic discourse on deepfakes has framed them “as a destabilizing platform that undermines a shared sense of social and political reality, enables the abuse and harassment of women online, and blurs the acceptable dichotomy between real and fake” (Yadlin-Segal and Oppenheim, 2020: 1). More broadly, Yadlin-Segal and Oppenheim summarize, looking back at news coverage:“…journalistic accounts built a clear narrative: machine agency leads to the demise of audiovisual content as an indicator of reality, which in turn leads to the loss of a shared social sense of truth, and the undermining of democratization of not only online media spaces, but the international political arena as a whole.” (Yadlin-Segal and Oppenheim, 2020: 10)…”

Section: The Context Of Deepfakes and Their Threatmentioning

confidence: 99%

Deepfakes, misinformation and disinformation and authenticity infrastructure responses: Impacts on frontline witnessing, distant witnessing, and civic journalism

Gregory

2021

Journalism

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Frontline witnessing and civic journalism are impacted by the rhetoric and the reality of misinformation and disinformation. This essay highlights key insights from activities of the human rights and civic journalism network WITNESS, as they seek to prepare for new forms of media manipulation, such as deepfakes, and to ensure that an emergent “authenticity infrastructure” is in place to respond to global needs for reliable information without creating additional harms. Based on global consultations on perceived threats and prioritized solutions, their efforts are primarily targeted towards synthetic media and deepfakes, which not only facilitate audiovisual falsification (including non-consensual sexual images) but also, by being embedded in societal dynamics of surveillance and civil society suppression, they challenge real footage and so undermine the credibility of civic media and frontline witnessing (also known as “liar’s dividend”). They do this within a global context where journalists and some distant witness investigators self-identify as lacking relevant skills and capacity, and face inequity in access to detection technologies. Within this context, “authenticity infrastructure” tracks media provenance, integrity, and manipulation from camera to edit to distribution, and so comes to provide “verification subsidies” that enable distant witnesses to properly interpret eye-witness footage. This “authenticity infrastructure” and related tools are rapidly moving from niche to mainstream in the form of initiatives the Content Authenticity Initiative and Coalition for Content Authenticity and Provenance, raising key questions about who participates in the production and dissemination of audiovisual information, under what circumstances and to which effect for whom. Provenance risks being weaponized unless key concerns are integrated into infrastructure proposals and implementation. Data may be used against vulnerable witnesses, or the absence of a trail, for legitimate privacy and technological access reasons, used to undermine credibility. Regulatory and extra-legal co-option are also a fear as securitized “fake news” laws proliferate. The investigation of both phenomena, deepfakes and emergent authenticity infrastructure(s), this paper argues, is important as it highlights the risks related both to the “information disorder” of deepfakes as they challenge the credibility and safety of frontline witnesses and to responses to such “disorder,” as they risk worsening inequities in access to tools for mitigation or increasing exposure to harms from technology infrastructure.

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Whose dystopia is it anyway? Deepfakes and social media regulation

Cited by 49 publications

References 31 publications

Silly Putty: Mobilization of the Known Across Technology-Enhanced Learning Spaces

Silly Putty: Mobilization of the Known Across Technology-Enhanced Learning Spaces

Deepfakes on Twitter: Which Actors Control Their Spread?

Deepfakes, misinformation and disinformation and authenticity infrastructure responses: Impacts on frontline witnessing, distant witnessing, and civic journalism

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