"Sora is Incredible and Scary": Emerging Governance Challenges of Text-to-Video Generative AI Models

• URL: http://arxiv.org/abs/2406.11859v1
• Date: Wed, 10 Apr 2024 02:03:59 GMT
• Title: "Sora is Incredible and Scary": Emerging Governance Challenges of Text-to-Video Generative AI Models
• Authors: Kyrie Zhixuan Zhou, Abhinav Choudhry, Ece Gumusel, Madelyn Rose Sanfilippo,
• Abstract summary: We report a qualitative social media analysis aiming to uncover people's perceived impact of and concerns about Sora's integration. We found that people were most concerned about Sora's impact on content creation-related industries. Potential regulatory solutions included law-enforced labeling of AI content and AI literacy education for the public.
• Score: 1.4999444543328293
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-to-video generative AI models such as Sora OpenAI have the potential to disrupt multiple industries. In this paper, we report a qualitative social media analysis aiming to uncover people's perceived impact of and concerns about Sora's integration. We collected and analyzed comments (N=292) under popular posts about Sora-generated videos, comparison between Sora videos and Midjourney images, and artists' complaints about copyright infringement by Generative AI. We found that people were most concerned about Sora's impact on content creation-related industries. Emerging governance challenges included the for-profit nature of OpenAI, the blurred boundaries between real and fake content, human autonomy, data privacy, copyright issues, and environmental impact. Potential regulatory solutions proposed by people included law-enforced labeling of AI content and AI literacy education for the public. Based on the findings, we discuss the importance of gauging people's tech perceptions early and propose policy recommendations to regulate Sora before its public release.

Related papers

• Examining the Prevalence and Dynamics of AI-Generated Media in Art Subreddits [13.343255875002459]
  We take steps towards examining the potential impact of AI-generated content on art-related communities on Reddit. We look at image-based posts made to these communities that are transparently created by AI, or comments in these communities that suspect authors of using generative AI. We show that AI content is more readily used by newcomers and may help increase participation if it aligns with community rules.
  arXiv  Detail & Related papers  (2024-10-09T17:41:13Z)
• Human Bias in the Face of AI: The Role of Human Judgement in AI Generated Text Evaluation [48.70176791365903]
  This study explores how bias shapes the perception of AI versus human generated content. We investigated how human raters respond to labeled and unlabeled content.
  arXiv  Detail & Related papers  (2024-09-29T04:31:45Z)
• Evaluating and Mitigating IP Infringement in Visual Generative AI [54.24196167576133]
  State-of-the-art visual generative models can generate content that bears a striking resemblance to characters protected by intellectual property rights. This happens when the input prompt contains the character's name or even just descriptive details about their characteristics. We develop a revised generation paradigm that can identify potentially infringing generated content and prevent IP infringement.
  arXiv  Detail & Related papers  (2024-06-07T06:14:18Z)
• Analysing the Public Discourse around OpenAI's Text-To-Video Model 'Sora' using Topic Modeling [0.0]
  This study aims to uncover the dominant themes and narratives surrounding Sora by conducting topic modeling analysis on a corpus of 1,827 Reddit comments. The comments were collected over a two-month period following Sora's announcement in February 2024. The results highlight prominent narratives around Sora's potential impact on industries and employment, public sentiment and ethical concerns, creative applications, and use cases in the media and entertainment sectors.
  arXiv  Detail & Related papers  (2024-05-30T01:55:30Z)
• From Sora What We Can See: A Survey of Text-to-Video Generation [10.204414498390575]
  Sora, developed by OpenAI, is capable of minute-level world-simulative abilities. Despite its notable successes, Sora still encounters various obstacles that need to be resolved.
  arXiv  Detail & Related papers  (2024-05-17T10:09:09Z)
• Sora OpenAI's Prelude: Social Media Perspectives on Sora OpenAI and the Future of AI Video Generation [30.556463355261695]
  This study investigates the public's perception of Sora OpenAI, a pioneering Gen-AI video generation tool, via social media discussions on Reddit. The analysis forecasts positive shifts in content creation, predicting that Sora will democratize video marketing and innovate game development. There are concerns about deepfakes and the potential for disinformation, underscoring the need for strategies to address disinformation and bias.
  arXiv  Detail & Related papers  (2024-03-02T00:16:22Z)
• Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models [59.54172719450617]
  Sora is a text-to-video generative AI model, released by OpenAI in February 2024. This paper presents a review of the model's background, related technologies, applications, remaining challenges, and future directions.
  arXiv  Detail & Related papers  (2024-02-27T03:30:58Z)
• DeepfakeArt Challenge: A Benchmark Dataset for Generative AI Art Forgery and Data Poisoning Detection [57.51313366337142]
  There has been growing concern over the use of generative AI for malicious purposes. In the realm of visual content synthesis using generative AI, key areas of significant concern has been image forgery and data poisoning. We introduce the DeepfakeArt Challenge, a large-scale challenge benchmark dataset designed specifically to aid in the building of machine learning algorithms for generative AI art forgery and data poisoning detection.
  arXiv  Detail & Related papers  (2023-06-02T05:11:27Z)
• Fairness in AI and Its Long-Term Implications on Society [68.8204255655161]
  We take a closer look at AI fairness and analyze how lack of AI fairness can lead to deepening of biases over time. We discuss how biased models can lead to more negative real-world outcomes for certain groups. If the issues persist, they could be reinforced by interactions with other risks and have severe implications on society in the form of social unrest.
  arXiv  Detail & Related papers  (2023-04-16T11:22:59Z)
• A Pathway Towards Responsible AI Generated Content [68.13835802977125]
  We focus on 8 main concerns that may hinder the healthy development and deployment of AIGC in practice. These concerns include risks from (1) privacy; (2) bias, toxicity, misinformation; (3) intellectual property (IP); (4) robustness; (5) open source and explanation; (6) technology abuse; (7) consent, credit, and compensation; (8) environment.
  arXiv  Detail & Related papers  (2023-03-02T14:58:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.