We present Queer in AI as a case study for community-led participatory design in AI. We examine how participatory design and intersectional tenets started and shaped this community's programs over the years. We discuss different challenges that emerged in the process, look at ways this organization has fallen short of operationalizing participatory and intersectional principles, and then assess the organization's impact. Queer in AI provides important lessons and insights for practitioners and theorists of participatory methods broadly through its rejection of hierarchy in favor of decentralization, success at building aid and programs by and for the queer community, and effort to change actors and institutions outside of the queer community. Finally, we theorize how communities like Queer in AI contribute to the participatory design in AI more broadly by fostering cultures of participation in AI, welcoming and empowering marginalized participants, critiquing poor or exploitative participatory practices, and bringing participation to institutions outside of individual research projects. Queer in AI's work serves as a case study of grassroots activism and participatory methods within AI, demonstrating the potential of community-led participatory methods and intersectional praxis, while also providing challenges, case studies, and nuanced insights to researchers developing and using participatory methods.

Which parts of a dataset will a given model find difficult? Recent work has shown that SGD-trained models have a bias towards simplicity, leading them to prioritize learning a majority class, or to rely upon harmful spurious correlations. Here, we show that the preference for "easy" runs far deeper: A model may prioritize any class or group of the dataset that it finds simple-at the expense of what it finds complex-as measured by performance difference on the test set. When subsets with different levels of complexity align with demographic groups, we term this difficulty disparity, a phenomenon that occurs even with balanced datasets that lack group/label associations. We show how difficulty disparity is a model-dependent quantity, and is further amplified in commonly-used models as selected by typical average performance scores. We quantify an amplification factor across a range of settings in order to compare disparity of different models on a fixed dataset. Finally, we present two real-world examples of difficulty amplification in action, resulting in worse-than-expected performance disparities between groups even when using a balanced dataset. The existence of such disparities in balanced datasets demonstrates that merely balancing sample sizes of groups is not sufficient to ensure unbiased performance. We hope this work presents a step towards measurable understanding of the role of model bias as it interacts with the structure of data, and call for additional model-dependent mitigation methods to be deployed alongside dataset audits.

Named Entity Recognition (NER) has seen significant progress in recent years, with numerous state-of-the-art (SOTA) models achieving high performance. However, very few studies have focused on the generation of entities' context. In this paper, we introduce CONTEXT-NER, a task that aims to generate the relevant context for entities in a sentence, where the context is a phrase describing the entity but not necessarily present in the sentence. To facilitate research in this task, we also present the EDGAR10-Q dataset, which consists of annual and quarterly reports from the top 1500 publicly traded companies. The dataset is the largest of its kind, containing 1M sentences, 2.8M entities, and an average of 35 tokens per sentence, making it a challenging dataset. We propose a baseline approach that combines a phrase generation algorithm with inferencing using a 220M language model, achieving a ROUGE-L score of 27% on the test split. Additionally, we perform a one-shot inference with ChatGPT, which obtains a 30% ROUGE-L, highlighting the difficulty of the dataset. We also evaluate models such as T5 and BART, which achieve a maximum ROUGE-L of 49% after supervised finetuning on EDGAR10-Q. We also find that T5-large, when pre-finetuned on EDGAR10-Q, achieve SOTA results on downstream finance tasks such as Headline, FPB, and FiQA SA, outperforming vanilla version by 10.81 points. To our surprise, this 66x smaller pre-finetuned model also surpasses the finance-specific LLM BloombergGPT-50B by 15 points. We hope that our dataset and generated artifacts will encourage further research in this direction, leading to the development of more sophisticated language models for financial text analysis

Rishi Sunak has used a trip to Washington to push the UK as a global centre for artificial intelligence regulation, insisting its record in the sector will make others listen to "this mid-sized country". Downing Street is hopeful that Joe Biden, whom Sunak was to meet at the White House on Thursday, will agree to US involvement in a UK-hosted global summit on AI safety in the autumn. The summit, formally announced by No 10 a day before the talks, is billed as a chance for leading companies and "like-minded countries" to discuss how to limit the potential risks of the technology's rapid advancement. It is designed to run alongside discussions on AI at last month's G7 summit in Japan, rather than competing. UK officials say the London gathering would be intended for companies and governments to start discussions over what sort of safeguards might be needed.

United Kingdom Prime Minister Rishi Sunak has begun a visit to Washington, DC, where topics like artificial intelligence (AI), the war in Ukraine and transatlantic trade are set to dominate two days of meetings. Sunak opened his visit on Wednesday by laying a wreath at the Tomb of the Unknown Soldier at Arlington National Cemetery, just outside Washington, DC, before meeting with congressional leaders. He is scheduled to join US President Joe Biden at the White House on Thursday. During his visit, Sunak said he would stress the UK's ability to play a "leadership role" in regulating AI. "Outside of the US, we are probably the leading AI nation amongst democratic countries. We have an ability to get regulation right to protect our citizens," he told the UK's TalkTV on Wednesday.

OpenAI CEO Sam Altman, the artificial intelligence lab behind ChatGPT, took questions from reporters following his congressional hearing, including defining "scary AI." Sen. Josh Hawley, R-Mo, unveiled a set of "guiding principles" ahead of any future artificial intelligence legislation Wednesday, seeking to "protect Americans' privacy" as the technology continues to develop. The Republican senator outlined five principles, first reported by Axios, aimed to "help set the course for the responsible development of American AI," as lawmakers figure out how to deal with current and future advancements. "Congress can and should act to protect Americans' privacy, stave off the harms of unchecked AI development, insulate kids from harmful impacts, and keep this valuable technology out of the hands of our adversaries," Hawley said in a statement. The recent leaps in easily-accessible AI technology like ChatGPT have led both lawmakers and industry leaders to recognize the need for regulation.

A former executive at TikTok's parent company, ByteDance, has alleged that the Chinese Communist party accessed user data from the social video app belonging to Hong Kong protesters and civil rights activists. Yintao Yu, a former head of engineering at ByteDance's US operation, claimed in a legal filing that a committee of Communist party members accessed TikTok data that included the users' network information, Sim card identifications and IP addresses in a bid to identify the individuals and their locations. The claims, in a wrongful dismissal lawsuit brought by Yu in a California court and reported by the Wall Street Journal, also allege the party accessed TikTok users' communications, monitored Hong Kong users who uploaded protest-related content and that Beijing-based ByteDance maintained a "backdoor channel" for the party to access US user data. Yu alleges in the filing that members of a Communist party committee inside ByteDance had access to a "superuser" credential which was also called a "God credential" and allowed them to view all data collected by ByteDance. The filing adds that when Yu was at ByteDance, between August 2017 and November 2018, TikTok stored all users' direct messages, search histories and content viewed by users.

ENTERING THE ARENA - Pence takes shot at Trump as he enters increasingly crowded Republican primary field. CAMPUS CHAOS - Two killed, several injured as gunfire breaks out after high school graduation, suspect in custody. TOP TARGETS - SPLC adds parents' rights groups to'Hate and Extremism' annual report. FAMILY MEN - America's men are poised to transform this nation for the better -- if we let them, writes Sen. Josh Hawley. BANKING ON IT - Industry responds to CFPB's warning on AI chatbots.

Sen. Marsha Blackburn, R-Tenn., shares her takeaways from Tuesday's AI hearing with OpenAI CEO Sam Altman. She also reveals what next steps she and her colleagues are prepared to take to protect consumer data amid the AI boom. Sen. Marsha Blackburn, R-Tenn., is calling on Congress to pass an internet user privacy standard as a first step toward making sure Americans are knowledgeable and their data safe amid the rapid advancement of artificial intelligence (AI) technology. Blackburn is one of four Republicans on the Senate Judiciary subcommittee on intellectual property (IP). The panel is holding a hearing Wednesday afternoon titled, "Artificial Intelligence and Intellectual Property – Part I: Patents, Innovation, and Competition."

Experts discuss what is at stake in the AI race between the United States and China, warning it could'dictate the future of humanity.' As artificial intelligence (AI) systems rapidly advance, the U.S. and China are both investing time and resources into developing the technology, but experts are divided on who controls the most advanced systems, who will be the front-runner to shape free speech and power in modern society. "The race between the U.S. and China, I think it's going to dictate the future of humanity," Dr. Michael Capps, the CEO of Diveplane, told Fox News Digital. "The Chinese government, Chinese military, and Chinese technology are all working in concert to win the AI race," he added. "In the United States, I would say that US technologists are working on it really hard, but not the government, and not the military. President Xi is 100% focused on it. Putin has said whoever wins the air race, wins World War III before it happens."