Document summarization is a task to shorten texts into concise and informative summaries. This paper introduces a novel dataset designed for summarizing multiple scientific articles into a section of a survey. Our contributions are: (1) SurveySum, a new dataset addressing the gap in domain-specific summarization tools; (2) two specific pipelines to summarize scientific articles into a section of a survey; and (3) the evaluation of these pipelines using multiple metrics to compare their performance. Our results highlight the importance of high-quality retrieval stages and the impact of different configurations on the quality of generated summaries.

In this paper, we introduce a novel postprocessing algorithm that is both model-agnostic Although rarely discussed, expecting the debiasing method and does not require the sensitive attribute at test to perform a low number of prediction changes is especially time. In addition, our algorithm is explicitly designed interesting in contexts where fairness is enforced while a to enforce minimal changes between biased model is already in production (Krco et al., 2023). In realworld and debiased predictions--a property that, applications, maintaining the integrity and reliability while highly desirable, is rarely prioritized as an of predictive models is crucial, especially when they have explicit objective in fairness literature. Our approach undergone rigorous validation and expert review. For example, leverages a multiplicative factor applied in non-life insurance pricing, experts commonly to the logit value of probability scores produced employ Generalized Additive Models (GAMs) with splines by a black-box classifier. We demonstrate the efficacy or polynomial regression on Generalized Linear Models to of our method through empirical evaluations, ensure that price are justifiable and align with both business comparing its performance against other four debiasing objectives and customer expectations (e.g., avoiding price algorithms on two widely used datasets in increases that could negatively impact customer satisfaction fairness research.

Tech expert Kurt Knutsson reveals how Figure's robot shows advanced manufacturing skills at BMW plant. HP is a household name when it comes to printers, but the company employs questionable practices to maximize profits. Much like Apple, HP aims to create a closed ecosystem, forcing you to use only its ink with its printers, especially if you opt into HP . Recently, I was at my in-laws' home and signed up for HP for them through the app only to discover that once you accept, the printer firmware is updated permanently. There's no way to undo it, and you're locked into using HP ink cartridges to print anything.

Klarna - which is based in Sweden, and has two UK offices - disclosed its job-cutting plans as it announced interim results which showed it increased its revenue by 27% to 13.3 billion Swedish krona ( 990 million). "Our proven scale efficiencies have been enhanced by our investment in AI, which has driven down operating expenses and improved gross profits," it said. It comes as unions have warned of mass job losses amid the growth of AI and are calling for legislation to protect workers. Mr Siemiatkowski said Klarna would reduce its headcount through what he called "natural attrition" - effectively a hiring freeze, where staff aren't replaced after they leave. Typically this means the people that remain are left with an increased workload. But Mr Siemiatkowski contended that AI would be replacing this work, and even claimed it was a potential "positive development" for some individuals who may be paid more.

South Korea has a dark history of digital sex crimes. In 2019 it emerged that men were using a Telegram chatroom to blackmail dozens of young women into performing sexual acts, in a scandal known as nth-room. The group's ring-leader, Cho Ju-bin, was sentenced to 42 years in jail. Online deepfake sex crimes have surged, according to South Korean police. A total of 297 cases were reported in the first seven months of this year, up from 180 in the whole of last year and 160 in 2021.

This thesis investigates three areas targeted at improving the reliability of machine learning; fairness in machine learning, strategic classification, and algorithmic robustness. Each of these domains has special properties or structure that can complicate learning. A theme throughout this thesis is thinking about ways in which a'plain' empirical risk minimization algorithm will be misleading or ineffective because of a mis-match between classical learning theory assumptions and specific properties of some data distribution in the wild. The overarching research goal for these related topics is to provide a crisp mathematical model for each learning scenario that exposes different failure modes and makes trade-offs between important metrics explicit in order to provide algorithmic advice or recommendations to practitioners and expose gaps for future research. By tuning our learning algorithms to be more distribution specific in these scenarios, the resulting learned system will exhibit higher utility and avoid catastrophic failure modes. This research is grounded in the theory of machine learning and is fundamentally mathematical in nature, with empirical support when appropriate. Theory is particularly important in these sensitive domains as it is unclear which poor behavior in deployed systems is a natural or benign consequence of a learning system with the underlying distribution,contrasting with problematic but correctable behavior caused by an error in algorithm design or implementation, how to mitigate these issues, or what a successful outcome even looks like in each problem. Theoretical understanding in each domain can help guide best practices and allow for the design of effective, reliable, and robust systems.

Detecting and attributing temperature increases due to climate change is crucial for understanding global warming and guiding adaptation strategies. The complexity of distinguishing human-induced climate signals from natural variability has challenged traditional detection and attribution (D&A) approaches, which seek to identify specific "fingerprints" in climate response variables. Deep learning offers potential for discerning these complex patterns in expansive spatial datasets. However, lack of standard protocols has hindered consistent comparisons across studies. We introduce ClimDetect, a standardized dataset of over 816k daily climate snapshots, designed to enhance model accuracy in identifying climate change signals. ClimDetect integrates various input and target variables used in past research, ensuring comparability and consistency. We also explore the application of vision transformers (ViT) to climate data, a novel and modernizing approach in this context. Our open-access data and code serve as a benchmark for advancing climate science through improved model evaluations.

The field of fair AI aims to counter biased algorithms through computational modelling. However, it faces increasing criticism for perpetuating the use of overly technical and reductionist methods. As a result, novel approaches appear in the field to address more socially-oriented and interdisciplinary (SOI) perspectives on fair AI. In this paper, we take this dynamic as the starting point to study the tension between computer science (CS) and SOI research. By drawing on STS and CSCW theory, we position fair AI research as a matter of 'organizational alignment': what makes research 'doable' is the successful alignment of three levels of work organization (the social world, the laboratory, and the experiment). Based on qualitative interviews with CS researchers, we analyze the tasks, resources, and actors required for doable research in the case of fair AI. We find that CS researchers engage with SOI research to some extent, but organizational conditions, articulation work, and ambiguities of the social world constrain the doability of SOI research for them. Based on our findings, we identify and discuss problems for aligning CS and SOI as fair AI continues to evolve.

What techniques can be used to verify compliance with international agreements about advanced AI development? In this paper, we examine 10 verification methods that could detect two types of potential violations: unauthorized AI training (e.g., training runs above a certain FLOP threshold) and unauthorized data centers. We divide the verification methods into three categories: (a) national technical means (methods requiring minimal or no access from suspected non-compliant nations), (b) access-dependent methods (methods that require approval from the nation suspected of unauthorized activities), and (c) hardware-dependent methods (methods that require rules around advanced hardware). For each verification method, we provide a description, historical precedents, and possible evasion techniques. We conclude by offering recommendations for future work related to the verification and enforcement of international AI governance agreements.

It was June 12, Lover's Day in Brazil. Leo Nunes, 24, had spent a few days talking to someone he met on Hornet, a popular gay dating app, before arranging their first encounter in Sao Paulo's middle-class Sacoma neighborhood. A security camera captured the moment that two men on a motorcycle showed up in the alley where he was waiting, grabbed his phone and shot him dead. The Nunes family, who shared details of the investigation with Reuters, said one suspect had been arrested. Sao Paulo police said they are investigating the shooting as a robbery resulting in a homicide, but did not provide further information or confirm if there had been an arrest.