Things to Do in L.A. Tap to enable a layout that focuses on the article. Times Investigation: Ex-Trump DOJ lawyers say'fraudulent' UC antisemitism probes led them to quit This is read by an automated voice. Please report any issues or inconsistencies here . Nine former DOJ attorneys investigating UC antisemitism told The Times they felt pressured to conclude that campuses had violated the civil rights of Jewish students and staff. The attorneys resigned during the course of their UC assignments, some concerned that they were being asked to violate ethical standards. UC says it is open to talks with the Trump administration to protect $17.5 billion in federal funding.

West Midlands Police has defended keeping silent over the significant hooliganism among Maccabi Tel Aviv fans, which it now confirms is the reason they were banned from attending the Europa League clash with Aston Villa. More than 700 officers from 20 police forces were deployed near Villa Park on Thursday, where hundreds took part in demonstrations over the controversial decision. When it emerged in October that fans of the Israeli club would not be welcome, senior MPs, including Prime Minister Sir Keir Starmer, said it amounted to antisemitism. Jack Angelides, CEO of Maccabi Tel Aviv, told the BBC their fans being banned meant it was time for some introspection and retrospection. Chief Constable Craig Guildford has been asked to appear before The Home Affairs Committee to explain the reasoning behind the ban, by Birmingham's Safety Advisory Group.

Detecting hateful content is a challenging and important problem. Automated tools, like machine-learning models, can help, but they require continuous training to adapt to the ever-changing landscape of social media. In this work, we evaluate eight open-source LLMs' capability to detect antisemitic content, specifically leveraging in-context definition. We also study how LLMs understand and explain their decisions given a moderation policy as a guideline. First, we explore various prompting techniques and design a new CoT-like prompt, Guided-CoT, and find that injecting domain-specific thoughts increases performance and utility. Guided-CoT handles the in-context policy well, improving performance and utility by reducing refusals across all evaluated models, regardless of decoding configuration, model size, or reasoning capability. Notably, Llama 3.1 70B outperforms fine-tuned GPT-3.5. Additionally, we examine LLM errors and introduce metrics to quantify semantic divergence in model-generated rationales, revealing notable differences and paradoxical behaviors among LLMs. Our experiments highlight the differences observed across LLMs' utility, explainability, and reliability. Code and resources available at: https://github.com/idramalab/quantify-llm-explanations

Tucker Carlson's friendly interview with white nationalist Nick Fuentes has led to a major reckoning in the Republican party. Nick Fuentes, a white nationalist known for his deeply antisemitic, racist, and misogynist worldview, just might be tearing the Republican party apart. The schism was triggered last Tuesday when former Fox News host Tucker Carlson released an in-depth interview with Fuentes, the leader of the so-called America First movement who has denied the Holocaust, praised Hitler, and shared deeply misogynistic views. During the interview, Fuentes waxed antisemitic about the threat apparently posed by "organized Jewry" in America, while Carlson slammed figures like senator Ted Cruz and former president George W. Bush as being "Christian Zionists" who have been "seized by this brain virus." Carlson was criticized by, among others, US Ambassador to Israel Mike Huckabee for giving Fuentes a platform, and the argument kicked into overdrive after Kevin Roberts, president of ...

Things to Do in L.A. Tap to enable a layout that focuses on the article. A Gaza Solidarity Encampment was created on the campus of Cal State Los Angeles in 2024. This is read by an automated voice. Please report any issues or inconsistencies here . The Trump administration launched an investigation into alleged antisemitism in the California State University system.

Elon Musk's AI firm has been forced to delete posts after the Grok chatbot praised Hitler and made a string of deeply antisemitic posts. The company xAI said it had removed'inappropriate' social media posts today following complaints from users. These posts followed Musk's announcement that he was taking measures to ensure the AI bot was more'politically incorrect'. Over the following days, the AI began repeatedly referring to itself as'MechaHitler' and said that Hitler would have'plenty' of solutions to'restore family values' to America. In a post on X, xAI wrote: 'We are aware of recent posts made by Grok and are actively working to remove the inappropriate posts. 'Since being made aware of the content, xAI has taken action to ban hate speech before Grok posts on X. 'xAI is training only truth-seeking and thanks to the millions of users on X, we are able to quickly identify and update the model where training could be improved.'

For a brief moment in November, the TheoBros, a network of militant Christian nationalist influencers, made news when Trump nominated one of their allies, former Fox News commentator Pete Hegseth, to lead the Department of Defense. Hegseth attends a church that is affiliated with the TheoBro movement, and he has cited TheoBro patriarch Doug Wilson, a pastor in Moscow, Idaho, as someone who has had a major influence on him. While the controversies surrounding Hegseth's alleged alcohol abuse and mismanagement of funds meant for veterans continue to make the news, the TheoBros have receded into the background. But as it turns, out, they are embroiled in a major controversy of their own. A simmering divide over how Christians should regard Judaism has ignited into a conflagration.

We created a fine-grained AI system for the detection of antisemitism. This Explainable AI will identify English and German anti-Semitic expressions of dehumanization, verbal aggression and conspiracies in online social media messages across platforms, to support high-level decision making.

Social biases in LLMs are usually measured via bias benchmark datasets. Current benchmarks have limitations in scope, grounding, quality, and human effort required. Previous work has shown success with a community-sourced, rather than crowd-sourced, approach to benchmark development. However, this work still required considerable effort from annotators with relevant lived experience. This paper explores whether an LLM (specifically, GPT-3.5-Turbo) can assist with the task of developing a bias benchmark dataset from responses to an open-ended community survey. We also extend the previous work to a new community and set of biases: the Jewish community and antisemitism. Our analysis shows that GPT-3.5-Turbo has poor performance on this annotation task and produces unacceptable quality issues in its output. Thus, we conclude that GPT-3.5-Turbo is not an appropriate substitute for human annotation in sensitive tasks related to social biases, and that its use actually negates many of the benefits of community-sourcing bias benchmarks.

Racism and intolerance on social media contribute to a toxic online environment which may spill offline to foster hatred, and eventually lead to physical violence. That is the case with online antisemitism, the specific category of hatred considered in this study. Tracking antisemitic themes and their associated terminology over time in online discussions could help monitor the sentiments of their participants and their evolution, and possibly offer avenues for intervention that may prevent the escalation of hatred. Due to the large volume and constant evolution of online traffic, monitoring conversations manually is impractical. Instead, we propose an automated method that extracts antisemitic themes and terminology from extremist social media over time and captures their evolution. Since supervised learning would be too limited for such a task, we created an unsupervised online machine learning approach that uses large language models to assess the contextual similarity of posts. The method clusters similar posts together, dividing, and creating additional clusters over time when sub-themes emerge from existing ones or new themes appear. The antisemitic terminology used within each theme is extracted from the posts in each cluster. Our experiments show that our methodology outperforms existing baselines and demonstrates the kind of themes and sub-themes it discovers within antisemitic discourse along with their associated terminology. We believe that our approach will be useful for monitoring the evolution of all kinds of hatred beyond antisemitism on social platforms.