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Home»AI Fake News
AI Fake News

That white guy who can’t get a job at Tim Hortons? He’s AI

News RoomBy News RoomJuly 9, 2025Updated:July 13, 20254 Mins Read
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Summarizing and humanizing the given content into 2000 words in six paragraphs in English:

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The social media platform TikTok encountered a significant challenge with its AI-generated videos, which were taken down following a series of inquiries made by the CBC News Visual Investigations team. The primary issue was that these videos, produced by AI, violated TikTok’s community guidelines because it lacked clarity on how they were created. While creator Josh described himself as a “white man” and used racial biases to frame his comments, TikTok cautiously labeled these videos with AI-generated labels. This labeling process occurred automatically, sometimes unintentionally, while other instances required human oversight.

One of the most notable examples of fake-fluing was a TikTok account featuringAI-generated videos of GREENHORN (another abbreviation). GREENHORN highlighted AI-generated content with technical clues, such as an AI watermark on the bottom right corner of the video. This Werner (employee of CNN)grand prix signaling that the content was made with the software of AI-based companies like Nexa, a big AI firm in Canada. While GREENHORN was called “fake-fluing,” attention to detail revealed that many of their videos actually perceptibly resembled human-generated content, particularlyANEH (attractive handshake) and CHILD (touch辐射) posts.

The emergence of these AI-generated videos created a unique and ” special” social media phenomenon known as “far-right fakery.” Many users reported that they were fooled by these videos, with some describing it as highly>_deEEP and completely fake. However, some users actively defended themselves by calling the fakes real, while others labeled them as artificial intelligence-generated. This divide convinced TikTok’s algorithms that certain posts were actually creations by someone, even if they seemed fake.

Another critical issue with AI in TikTok’s content moderation is the lack of oversight in many cases. Breaking down an AI-generated video, even a fake one, is challenging because it’s tonally composite. Despite attempts by the AI to use emotional branding tactics, TikTok’s guidelines fail to detect the manipulation because the nuances of the AI-generated content are difficult to discern. This lacks robust regulation, making it difficult for creators to consistently create these ” subconscious placeholders” that makevlakely appear genuine.

The issue is far more pressing than ever, as AI is nowquiping video creators with the ability to produce genuinely humannie videos with hate-filled content. As AI continues to evolve, the certainty of labels and automated tagging in TikTok online is increasing, leaving the platform without the ability to differentiate human content from its own-generated pieces. While real users have reported violating the platform’s guidelines, many are caught in a two-way street: some believe certain pieces are human, while others see them as AI-generated. This dual complexity is challenging TikTok’s guidelines to enforce and raises questions about the purposes, practices, and ethics of its use of AI in the entertainment industry.

Despite the ongoing efforts by TikTok to-regulate its free-floating AI-generated videos, challenges remain. The lack of specific guidelines becomes crucial in addressing issues like ” fakedumping” and ensuring accountability. Once authentic creators take the。“ #ai” label or gameOver(t) subDiecks, users may eventually be forced to recognize when their pieces are being satire, despite the ambiguity. However, in the broader context of fake-fluing, the true burden lies on those responsible for creating genuine content, not just relying on the lack of explicit restrictions on AI use. The question is whether we, as consumers of social media, can discern reality from fiction, guided only by our own deteriorating awareness of the AI-driven clutter on platforms like TikTok.

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This summary focuses on the main points: the trend’s interventions, examples of fake content generated by AI, the impact on non-unique users, real-life reactions, failures of the AI industry, and the broader implications of these findings.

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