The Automated Verification of Textual Claims (AVeriTeC) shared task asks participants to retrieve evidence and predict veracity for real-world claims checked by fact-checkers. Evidence can be found either via a search engine, or via a knowledge store provided by the organisers. Submissions are evaluated using AVeriTeC score, which considers a claim to be accurately verified if and only if both the verdict is correct and retrieved evidence is considered to meet a certain quality threshold. The shared task received 21 submissions, 18 of which surpassed our baseline. The winning team was TUDA_MAI with an AVeriTeC score of 63%. In this paper we describe the shared task, present the full results, and highlight key takeaways from the shared task.

This study examines the influence of Google's search algorithm on news diversity by analyzing search results in Brazil, the UK, and the US. It explores how Google's system preferentially favors a limited number of news outlets. Utilizing algorithm auditing techniques, the research measures source concentration with the Herfindahl-Hirschman Index (HHI) and Gini coefficient, revealing significant concentration trends. The study underscores the importance of conducting horizontal analyses across multiple search queries, as focusing solely on individual results pages may obscure these patterns. Factors such as popularity, political bias, and recency were evaluated for their impact on news rankings. Findings indicate a slight leftward bias in search outcomes and a preference for popular, often national outlets. This bias, combined with a tendency to prioritize recent content, suggests that Google's algorithm may reinforce existing media inequalities. By analyzing the largest dataset to date -- 221,863 search results -- this research provides comprehensive, longitudinal insights into how algorithms shape public access to diverse news sources.

The manner in which different racial and gender groups are portrayed in news coverage plays a large role in shaping public opinion. As such, understanding how such groups are portrayed in news media is of notable societal value, and has thus been a significant endeavour in both the computer and social sciences. Yet, the literature still lacks a longitudinal study examining both the frequency of appearance of different racial and gender groups in online news articles, as well as the context in which such groups are discussed. To fill this gap, we propose two machine learning classifiers to detect the race and age of a given subject. Next, we compile a dataset of 123,337 images and 441,321 online news articles from New York Times (NYT) and Fox News (Fox), and examine representation through two computational approaches. Firstly, we examine the frequency and prominence of appearance of racial and gender groups in images embedded in news articles, revealing that racial and gender minorities are largely under-represented, and when they do appear, they are featured less prominently compared to majority groups. Furthermore, we find that NYT largely features more images of racial minority groups compared to Fox. Secondly, we examine both the frequency and context with which racial minority groups are presented in article text. This reveals the narrow scope in which certain racial groups are covered and the frequency with which different groups are presented as victims and/or perpetrators in a given conflict. Taken together, our analysis contributes to the literature by providing two novel open-source classifiers to detect race and age from images, and shedding light on the racial and gender biases in news articles from venues on opposite ends of the American political spectrum.

This special edition is part of our Guest Prudie series, where we ask smart, thoughtful people to step in as Prudie for the day and give you advice. Today's columnist is number one New York Times-bestselling author Leigh Bardugo. She is the author of the books The Familiar, Ninth House and the creator of the Grishaverse (now a Netflix original series) which spans the Shadow and Bone trilogy, the Six of Crows duology, the King of Scars duology. Her short fiction has appeared in multiple anthologies including The Best American Science Fiction and Fantasy. She lives in Los Angeles and is an associate fellow of Pauli Murray College at Yale University. We asked Bardugo to weigh in on "romantic" gestures gone wrong, conversational vampires, and vocal dogs: I recently met a man on a dating app. We hit it off quickly. We were texting all of the time about work, writing, and the world--often getting pretty flirty. I was having tons of fun. He was charming and seemed to me conspicuously brilliant.

'The Five' co-hosts discus Elon Musk's prediction that jobs will become like a'hobby' as AI progresses. 'FUTURE OF MEDICINE': Elon Musk is urging people to submit their medical scans to Grok for analysis, but doctors advise using caution when relying on artificial intelligence for health care insights. AI ART FOR SALE: Ai-Da, the world's first ultra-realistic robot artist, has produced a striking portrait of computing pioneer Alan Turing that will go under the hammer this month. SAN DIEGO, CA - JULY 14: Actor Robert Downey Jr. arrives at the "Iron Man 3" panel with Marvel Studios during Comic-Con International 2012 at San Diego Convention Center on July 14, 2012 in San Diego, California. IRON MAN'S FIGHT: Robert Downey Jr. might be devoid of iron, but he's sure got some steel.

Chatbots are computer programme which can simulate human conversation. The recent rapid development in artificial intelligence (AI) have seen them become much more sophisticated and realistic, prompting more companies to set up platforms where users can create digital "people" to interact with. It has terms of service which ban using the platform to "impersonate any person or entity" and in its "safety centre" the company says its guiding principle is that its "product should never produce responses that are likely to harm users or others". It says it uses automated tools and user reports to identify uses that break its rules and is also building a "trust and safety" team. But it notes that "no AI is currently perfect" and safety in AI is an "evolving space". Character.ai is currently the subject of a lawsuit brought by Megan Garcia, a woman from Florida whose 14-year-old son, Sewell Setzer, took his own life after becoming obsessed with an AI avatar inspired by a Game of Thrones character.

This paper introduces a novel approach that leverages Large Language Models (LLMs) and Generative Agents to enhance time series forecasting by reasoning across both text and time series data. With language as a medium, our method adaptively integrates social events into forecasting models, aligning news content with time series fluctuations to provide richer insights. Specifically, we utilize LLM-based agents to iteratively filter out irrelevant news and employ human-like reasoning to evaluate predictions. This enables the model to analyze complex events, such as unexpected incidents and shifts in social behavior, and continuously refine the selection logic of news and the robustness of the agent's output. By integrating selected news events with time series data, we fine-tune a pre-trained LLM to predict sequences of digits in time series. The results demonstrate significant improvements in forecasting accuracy, suggesting a potential paradigm shift in time series forecasting through the effective utilization of unstructured news data.

Effective spatio-temporal measurements of water surface elevation (water waves) in laboratory experiments are essential for scientific and engineering research. Existing techniques are often cumbersome, computationally heavy and generally suffer from limited wavenumber/frequency response. To address these challenges a novel method was developed, using polarization filter equipped camera as the main sensor and Machine Learning (ML) algorithms for data processing [1,2]. The developed method training and evaluation was based on in-house made supervised dataset. Here we present this supervised dataset of polarimetric images of the water surface coupled with the water surface elevation measurements made by a linear array of resistance-type wave gauges (WG). The water waves were mechanically generated in a laboratory waves basin, and the polarimetric images were captured under an artificial light source. Meticulous camera and WGs calibration and instruments synchronization supported high spatio-temporal resolution. The data set covers several wavefield conditions, from simple monochromatic wave trains of various steepness, to irregular wavefield of JONSWAP prescribed spectral shape and several wave breaking scenarios. The dataset contains measurements repeated in several camera positions relative to the wave field propagation direction.

In this paper, we create "Love in Action" (LIA), a body language-based social game utilizing video cameras installed in public spaces to enhance social relationships in real-world. In the game, participants assume dual roles, i.e., requesters, who issue social requests, and performers, who respond social requests through performing specified body languages. To mediate the communication between participants, we build an AI-enhanced video analysis system incorporating multiple visual analysis modules like person detection, attribute recognition, and action recognition, to assess the performer's body language quality. A two-week field study involving 27 participants shows significant improvements in their social friendships, as indicated by Self-reported questionnaires. Moreover, user experiences are investigated to highlight the potential of public video cameras as a novel communication medium for socializing in public spaces.

Large-scale deployment of large language models (LLMs) in various applications, such as chatbots and virtual assistants, requires LLMs to be culturally sensitive to the user to ensure inclusivity. Culture has been widely studied in psychology and anthropology, and there has been a recent surge in research on making LLMs more culturally inclusive in LLMs that goes beyond multilinguality and builds on findings from psychology and anthropology. In this paper, we survey efforts towards incorporating cultural awareness into text-based and multimodal LLMs. We start by defining cultural awareness in LLMs, taking the definitions of culture from anthropology and psychology as a point of departure. We then examine methodologies adopted for creating cross-cultural datasets, strategies for cultural inclusion in downstream tasks, and methodologies that have been used for benchmarking cultural awareness in LLMs. Further, we discuss the ethical implications of cultural alignment, the role of Human-Computer Interaction in driving cultural inclusion in LLMs, and the role of cultural alignment in driving social science research. We finally provide pointers to future research based on our findings about gaps in the literature.