Earlier this year, I attended a shooting competition for queer, often trans, very online misfits. Then Charlie Kirk was killed. This isn't the story I set out to write. I was going to talk about a pretty feel-good firearms competition I went to earlier this year, where trans and queer people made up about a quarter of participants and the unofficial rule was you're not allowed to be a bigot. I was going to describe the strange and whimsical mix of subcultures people embraced there--like polyamory and Mad Max cosplay--wrapped up in pro-LGBT and Black Lives Matter patches. Then Charlie Kirk was killed. Suddenly I found myself wondering if I should write this story at all. If doing so would put my sources--gun-loving trans people in Trump's America--in danger.

The new AI-powered Wikipedia competitor falsely claims that pornography worsened the AIDS epidemic and that social media may be fueling a rise in transgender people. On Monday, Elon Musk's xAI startup launched Grokipedia, which the billionaire is pitching as an AI-generated alternative to the crowdsourced encyclopedia Wikipedia. Musk first announced the project in late September on his social media platform X, saying it would be "a massive improvement over Wikipedia," and "a necessary step towards the xAI goal of understanding the Universe." Musk said last week that he had delayed the launch of Grokipedia because his team needed "to do more work to purge out the propaganda." When Grokipedia eventually dropped on Monday, WIRED was initially unable to access the website and received an automated message that it was blocked.

Cancel Culture Comes for Artists Who Posted About Charlie Kirk's Death A episode was taken off air, a DC comic series was canceled, and several artists were fired in the aftermath of the shooting. Almost immediately after she posted about the shooting of Charlie Kirk, author and transwoman Gretchen Felker-Martin started having second thoughts. Felker-Martin, who wrote the latest iteration of DC Comics' series, said "thoughts and prayers you Nazi bitch" on Bluesky in response to the killing of Kirk, a right-wing influencer and Trump ally who was staunchly anti-trans rights. "Hope the bullet's okay after touching Kirk," she added. Kirk died after being shot at a stop on his American Comeback Tour organized by the conservative youth organization he founded, Turning Point USA.

Large language models (LLMs) rely heavily on web-scale datasets like Common Crawl, which provides over 80\% of training data for some modern models. However, the indiscriminate nature of web crawling raises challenges in data quality, safety, and ethics. Despite the critical importance of training data quality, prior research on harmful content has been limited to small samples due to computational constraints. This project presents a framework for indexing and analyzing LLM training datasets using an ElasticSearch-based pipeline. We apply it to SwissAI's FineWeb-2 corpus (1.5TB, four languages), achieving fast query performance--most searches in milliseconds, all under 2 seconds. Our work demonstrates real-time dataset analysis, offering practical tools for safer, more accountable AI systems.

AI safety is a contentious topic. While some prominent figures of the AI community have argued that destructive general artificial intelligence (AI) is on the horizon, others derided their warning as a marketing stunt to sell large language models (LLMs). "If the call for'AI safety' is couched in terms of protecting humanity from rogue AIs, it very conveniently displaces accountability away from the corporations scaling harm in the name of profits," tweeted Emily Bender, a professor of computational linguistics at the University of Washington. Focusing on potential future harm from ever more powerful AI systems distracts from harm that is already happening today. Most of us do not set out to make software that is actively harmful.

The recent proliferation of short form video social media sites such as TikTok has been effectively utilized for increased visibility, communication, and community connection amongst trans/nonbinary creators online. However, these same platforms have also been exploited by right-wing actors targeting trans/nonbinary people, enabling such anti-trans actors to efficiently spread hate speech and propaganda. Given these divergent groups, what are the differences in network structure between anti-trans and pro-trans communities on TikTok, and to what extent do they amplify the effects of anti-trans content? In this paper, we collect a sample of TikTok videos containing pro and anti-trans content, and develop a taxonomy of trans related sentiment to enable the classification of content on TikTok, and ultimately analyze the reply network structures of pro-trans and anti-trans communities. In order to accomplish this, we worked with hired expert data annotators from the trans/nonbinary community in order to generate a sample of highly accurately labeled data. From this subset, we utilized a novel classification pipeline leveraging Retrieval-Augmented Generation (RAG) with annotated examples and taxonomy definitions to classify content into pro-trans, anti-trans, or neutral categories. We find that incorporating our taxonomy and its logics into our classification engine results in improved ability to differentiate trans related content, and that Results from network analysis indicate many interactions between posters of pro-trans and anti-trans content exist, further demonstrating targeting of trans individuals, and demonstrating the need for better content moderation tools

Online hate detection suffers from biases incurred in data sampling, annotation, and model pre-training. Therefore, measuring the averaged performance over all examples in held-out test data is inadequate. Instead, we must identify specific model weaknesses and be informed when it is more likely to fail. A recent proposal in this direction is HateCheck, a suite for testing fine-grained model functionalities on synthesized data generated using templates of the kind "You are just a [slur] to me." However, despite enabling more detailed diagnostic insights, the HateCheck test cases are often generic and have simplistic sentence structures that do not match the real-world data. To address this limitation, we propose GPT-HateCheck, a framework to generate more diverse and realistic functional tests from scratch by instructing large language models (LLMs). We employ an additional natural language inference (NLI) model to verify the generations. Crowd-sourced annotation demonstrates that the generated test cases are of high quality. Using the new functional tests, we can uncover model weaknesses that would be overlooked using the original HateCheck dataset.

Now is the perfect time to play 2020's story-driven adventure game Tell Me Why: in honour of Pride month, it's currently free to download. Developer Don't Nod's tale follows a trans man returning to his childhood home and confronting his family's past. A major video game that centres any trans character is a rarity to celebrate, but Tyler Ronan doesn't feel tokenistic; he is part of a mature and complex story. Tell Me Why feels like a necessary counterbalance to a wider climate that seems desperate to make gender-diverse people feel marginalised and forgotten. Tyler, a trans man, and his twin cisgender sister, Alyson, spend much of the game exploring the mysteries behind their mother's death.

How to Do It is Slate's sex advice column. Send it to Stoya and Rich here. My partner and I (man and woman in our mid-30s) want to open profiles on an adult dating site (Feeld, probably?) to connect with couples and singles. We've had ethically non-monogamous encounters at adult resorts, but haven't tried a dating site to meet people closer to home in hopes of landing on more "social swinging" relationships. There are a wealth of swinging/lifestyle podcasts with episodes about dating profiles, and omitting your face from "public" photos on the site (that is, visible to all members) is uniform advice. Of course, most often this is to avoid being identified on the site.

In brief AI cameras inside Amazon's delivery trucks are denying drivers' bonus pay for errors they shouldn't be blamed for, it's reported. The e-commerce giant installed the equipment in its vehicles earlier this year. The devices watch the road and the driver, and send out audio alerts if they don't like the way their humans are driving. One man in Los Angeles told Vice that when he gets cut off by other cars, the machine would sense the other vehicle suddenly right in front of him, and squawk: "Maintain safe distance!" Logs of the audio alerts and camera footage are relayed back to Amazon, and it automatically decides whether drivers deserve to get bonuses or not from their performance on the road.