Content moderation is a typical intervention strategy for Content moderation is a widely used strategy to regulating online communities on social media prevent the dissemination of irregular information on platforms, to ensure that user-generated content social media platforms. Despite extensive research on complies with the platforms' policies and community developing automated models to support decisionmaking standards (Gillespie, 2020). in content moderation, there remains a notable With the advancement of AI technologies and the scarcity of studies that integrate the rules of online increasing workload associated with online moderation communities into content moderation. This study (Batrinca & Treleaven, 2015), online platforms are addresses this gap by proposing a community rulebased increasingly adopting machine learning and/or deep content moderation framework that directly learning-based techniques to automate content integrates community rules into the moderation of usergenerated moderation, particularly to address its scalability issue content.

While traditional discussions of epistemic injustice have While algorithms have traditionally been leveraged to primarily centered on interpersonal human interactions present and organize human-generated content, the advent (McKinnon 2017; Tsosie 2012), existing research on algorithmic of generative AI has started to fundamentally shift this epistemic injustice has largely been limited to epistemic paradigm. Generative AI models can now create content - injustices produced by decision-making and classification spanning text, imagery, and beyond - that resembles that of algorithms. However, we argue that the distinctive authors, journalists, painters, or photographers. In this paper, characteristics of generative AI give rise to novel forms of we take generative AI to be the class of machine learning epistemic injustice that necessitate a dedicated analytical models trained on massive amounts of data, typically media framework. To address this, we expand upon the established such as text, images, audio or video, in order to produce philosophical discourse on epistemic injustice and introduce representative instances of such media (García-Peñalvo and an account of "generative algorithmic epistemic injustice," Vázquez-Ingelmo 2023).

For the past few nights, I've concerned myself with the private lives of autonomous vehicles. It started when I read a news story about a San Francisco apartment complex whose residents were repeatedly awoken at 4 a.m. by honking self-driving taxis. The building overlooks an open-air parking lot that Waymo recently leased to store its vehicles. In the wee hours of the morning--between ferrying home overserved bar crawlers and picking up commuters during the morning rush hour--dozens of the autonomous white sedans fill the lot, power down, and wait to be summoned. Sometimes, too many awaken at the same time and back up while trying to make their way to the exit, only to find the lanes clogged by their brethren.

Secret Level is a gaming-inspired anthology series coming to Prime Video on December 15. The upcoming Amazon title is from the same team behind Netflix's Love, Death and Robots. From the teaser released during Gamescom, this new project will be continuing the Blur Studio specialty for creating masterful animated works for an adult audience. The creative team was looking to inspire "nerd joy" with Love, Death and Robots and from the first glimpse, Secret Level seems like a natural progression of that goal. Each of the 15 stories in the show are inspired by a different game.

Condé Nast and OpenAI announced a multi-year partnership on Tuesday to display content from the publisher's brands such as the Vogue, Wired and the New Yorker within the AI startup's products, including ChatGPT and its SearchGPT prototype. The financial terms of the deal were not disclosed. The Microsoft-backed, Sam Altman-led firm has signed similar deals with Time magazine, the Financial Times, Business Insider owner Axel Springer, France's Le Monde and Spain's Prisa Media over the past few months. The deals give OpenAI access to the large archives of text owned by the publishers, which are necessary both for training large language models like ChatGPT and for finding real-time information. OpenAI launched its AI-powered search engine SearchGPT in July, with real-time access to information from the internet, making an incursion on territory long dominated by Google.

Condé Nast, the media conglomerate that owns publications like The New Yorker, Vogue and Wired, has announced a multi-year partnership OpenAI to display content from Condé Nast titles in ChatGPT as well as SearchGPT, the company's prototype AI-powered search engine. The partnership comes amid growing concerns over the unauthorized use of publishers' content by AI companies. Last month, Condé Nast sent a cease-and-desist letter to AI search startup Perplexity, accusing it of plagiarism for using its content to generate answers. "Over the last decade, news and digital media have faced steep challenges as many technology companies eroded publishers' ability to monetize content, most recently with traditional search," Condé Nast CEO Roger Lynch wrote to employees in a memo that was first reported by Semafor's Max Tani. "Our partnership with OpenAI begins to make up for some of that revenue, allowing us to continue to protect and invest in our journalism and creative endeavors."

Condé Nast and OpenAI have struck a multi-year deal that will allow the AI giant to use content from the media giant's roster of properties--which includes the New Yorker, Vogue, Vanity Fair, Bon Appetit, and, yes, WIRED. The deal will allow OpenAI to surface stories from these outlets in both ChatGPT and the new SearchGPT prototype. "It's crucial that we meet audiences where they are and embrace new technologies while also ensuring proper attribution and compensation for use of our intellectual property," Condé Nast CEO Roger Lynch wrote in a company-wide email. Lynch pointed to ongoing turmoil within the publishing industry while discussing the deal, noting that technology companies have made it harder for publishers to make money, most recently with changes to traditional search. "Our partnership with OpenAI begins to make up for some of that revenue, allowing us to continue to protect and invest in our journalism and creative endeavors," he wrote.

Once upon a time in the tail-end of the last century, there was something called the Y2K bug. This bit of computer code was supposed to herald a global robot apocalypse at the stroke of midnight when 1999 became the year 2000 because of, uh, clock dates or something. The film imagines a New Year's Eve of 1999 in which the computers really did turn on humanity. It's written and directed by SNL alum Kyle Mooney, who made the fantastic and underrated Brigsby Bear. As you can see from the trailer, it's a 1990s teen party comedy, like Can't Hardly Wait, but also an apocalyptic horror film.

Lucasfilm has decided not to renew The Acolyte for a second season, according to Deadline and Variety. Fans won't get to see how the show was supposed to end and won't get to know how the plotlines its creator, Leslye Headland (Russian Doll), teased at the end of the first season would unravel. Engadget Senior Editor Devindra Hardawar called The Acolyte "Star Wars at its best" in his review, discussed how unique its premise was, and drew parallels between the series and Crouching Tiger, Hidden Dragon. Deadline says the show had a strong start and garnered 4.8 million views in the first day it became available for streaming, reaching 11.1 million views after five days. However, viewership fell in the coming weeks, and its finale was reportedly the poorest performing finale for a Star Wars series.

In the era dominated by information overload and its facilitation with Large Language Models (LLMs), the prevalence of misinformation poses a significant threat to public discourse and societal well-being. A critical concern at present involves the identification of machine-generated news. In this work, we take a significant step by introducing a benchmark dataset designed for neural news detection in four languages: English, Turkish, Hungarian, and Persian. The dataset incorporates outputs from multiple multilingual generators (in both, zero-shot and fine-tuned setups) such as BloomZ, LLaMa-2, Mistral, Mixtral, and GPT-4. Next, we experiment with a variety of classifiers, ranging from those based on linguistic features to advanced Transformer-based models and LLMs prompting. We present the detection results aiming to delve into the interpretablity and robustness of machine-generated texts detectors across all target languages.