As AI becomes more integral in our lives, the need for transparency and responsibility grows. While natural language explanations (NLEs) are vital for clarifying the reasoning behind AI decisions, evaluating them through human judgments is complex and resource-intensive due to subjectivity and the need for fine-grained ratings. This study explores the alignment between ChatGPT and human assessments across multiple scales (i.e., binary, ternary, and 7-Likert scale). We sample 300 data instances from three NLE datasets and collect 900 human annotations for both informativeness and clarity scores as the text quality measurement. We further conduct paired comparison experiments under different ranges of subjectivity scores, where the baseline comes from 8,346 human annotations. Our results show that ChatGPT aligns better with humans in more coarse-grained scales. Also, paired comparisons and dynamic prompting (i.e., providing semantically similar examples in the prompt) improve the alignment. This research advances our understanding of large language models' capabilities to assess the text explanation quality in different configurations for responsible AI development.

Fact-checking is the task of verifying the factuality of a given claim by examining the available evidence. High-quality evidence plays a vital role in enhancing fact-checking systems and facilitating the generation of explanations that are understandable to humans. However, the provision of both sufficient and relevant evidence for explainable fact-checking systems poses a challenge. To tackle this challenge, we propose a method based on a Large Language Model to automatically retrieve and summarize evidence from the Web. Furthermore, we construct RU22Fact, a novel multilingual explainable fact-checking dataset on the Russia-Ukraine conflict in 2022 of 16K samples, each containing real-world claims, optimized evidence, and referenced explanation. To establish a baseline for our dataset, we also develop an end-to-end explainable fact-checking system to verify claims and generate explanations. Experimental results demonstrate the prospect of optimized evidence in increasing fact-checking performance and also indicate the possibility of further progress in the end-to-end claim verification and explanation generation tasks.

This scientific report presents a novel methodology for the early prediction of important political events using News datasets. The methodology leverages natural language processing, graph theory, clique analysis, and semantic relationships to uncover hidden predictive signals within the data. Initially, we designed a preliminary version of the method and tested it on a few events. This analysis revealed limitations in the initial research phase. We then enhanced the model in two key ways: first, we added a filtration step to only consider politically relevant news before further processing; second, we adjusted the input features to make the alert system more sensitive to significant spikes in the data. After finalizing the improved methodology, we tested it on eleven events including US protests, the Ukraine war, and French protests. Results demonstrate the superiority of our approach compared to baseline methods. Through targeted refinements, our model can now provide earlier and more accurate predictions of major political events based on subtle patterns in news data.

Optimal experimental design (OED) provides a systematic approach to quantify and maximize the value of experimental data. Under a Bayesian approach, conventional OED maximizes the expected information gain (EIG) on model parameters. However, we are often interested in not the parameters themselves, but predictive quantities of interest (QoIs) that depend on the parameters in a nonlinear manner. We present a computational framework of predictive goal-oriented OED (GO-OED) suitable for nonlinear observation and prediction models, which seeks the experimental design providing the greatest EIG on the QoIs. In particular, we propose a nested Monte Carlo estimator for the QoI EIG, featuring Markov chain Monte Carlo for posterior sampling and kernel density estimation for evaluating the posterior-predictive density and its Kullback-Leibler divergence from the prior-predictive. The GO-OED design is then found by maximizing the EIG over the design space using Bayesian optimization. We demonstrate the effectiveness of the overall nonlinear GO-OED method, and illustrate its differences versus conventional non-GO-OED, through various test problems and an application of sensor placement for source inversion in a convection-diffusion field.

Former House Speaker Nancy Pelosi has invested up to 5 million in a San Francisco-based company, adding to her successful portfolio of Big Tech. Documents revealed Pelosi's transaction with privately held Databricks, which is a software company based on AI technology, took place on March 3 and was disclosed on March 21. Databricks is just the latest newcomer to Pelosi's long list of companies, but there are eight major names that she has invested 16 million in since 2022. While she has not broken any laws by buying and selling stocks, many Americans and other government officials see the investments as conflicts of interest since she has access to confidential intelligence and the power to impact businesses. Documents revealed Pelosi's transaction with privately held Databricks, which is a software company based on AI technology, took place on March 3 and disclosed on March 21 Databricks is just the latest newcomer to Pelosi's long list of companies, but there are eight major names that she has invested up to 16.1 million in since 2022 Databricks, founded in 2013, raised 500 million last year based on a 43 billion valuation.

There are 27 million refugees around the world.22 The United Nations High Commissioner for Refugees (UNHCR) considers over 1.4 million to be in need of resettlement, that is, permanent relocation from a temporary country of asylum to a third country.21 Resettlement is mainly targeted at the most vulnerable of refugees, such as children at risk, survivors of violence and torture, and those with urgent medical needs. Dozens of countries around the world resettle refugees, but every year the number of refugees in need of resettlement far exceeds the number that is actually resettled. In 2019, for example, only around 63,000 refugees were resettled.21

Iain Duncan Smith has said he was impersonated by a pro-China "wolf warrior" and has called for the country to be labelled a threat to UK security. The former Tory leader said on Monday that the "wolf warrior", a term used for combative proponents of the Chinese government, had impersonated him and sent emails to politicians around the world suggesting he had changed his views about Beijing. He was speaking at a press conference with two other MPs who were briefed by security services on Monday about cyber-attacks against them by actors linked to China. Tim Loughton, another Tory MP who has been critical of the Chinese government, said he was "particularly concerned" about Uyghur rights activists whose families were contacted by pro-Beijing figures after they associated with critical MPs. Later on Monday, ministers are expected to announce that Beijing-linked hackers were behind a cyber-attack on the Electoral Commission which exposed the personal data of 40 million voters, as well as 43 individuals including MPs and peers.

Figure has developed a full-body humanoid robot, Figure-01, that can walk, talk and interact. Le Van Tam is no stranger to how the vagaries of global trade can determine the fortunes of small coffee farmers like him. He first planted coffee in a patch of land outside Buon Ma Thuot city in Vietnam's Central Highland region in 1995. For years, his focus was on quantity, not quality. Tam used ample amounts of fertilizer and pesticides to boost his yields, and global prices determined how well he did.

On Friday, Russia fired dozens of missiles at Ukraine, hitting a dam and leaving a million Ukrainians without power, in the wake of fierce Ukrainian bombardments on Russian border regions. The Russian authorities said a Ukrainian drone attack had caused a fire at a large power plant in Rostov.

Russia launched missiles against Kyiv for the third time in five days, part of an apparent escalation of the aerial bombardment of Ukrainian cities as the war stretches into its third year with the front line largely stationary. Five people were injured in the strike on the Ukrainian capital, with two of them taken to hospital, Kyiv Mayor Vitali Klitschko said. Russia fired two ballistic missiles at Kyiv from occupied Crimea in the daylight attack, but both were intercepted above the city, said Serhiy Popko, the head of the city's military administration. Multiple explosions were heard in the capital, in the latest scare for residents. Ukraine has been appealing to its allies for months for greater air defence capabilities as Russia steps up its attacks across the country.