bluuebunny/crossref_metadata_2025 huggingface dataset:
ФИЗИКО-ХИМИЧЕСКИЕ СВОЙСТВА СУПРАМОЛЕКУЛЯРНОГО КОМПЛЕКСА АРТЕСУНАТА С ГЛИЦИРРИЗИНАТОМ НАТРИЯ И ЕГО ОПИСТОРХИЦИДНАЯ АКТИВНОСТЬ IN VITRO В ОТНОШЕНИИ ТРЕМАТОДЫ OPISTHORCHIS FELINEUS
Authors: Д.В. ПОНОМАРЁВ , М.Н. ЛЬВОВА , Д.Ф. АВГУСТИНОВИЧ , Е.С. МЕТЕЛЕВА , В.И. ЕВСЕЕНКО , О.Ю. СЕЛЮТИНА , Н.Э. ПОЛЯКОВ , А.В. ДУШКИН
Published: November 2024

Механохимическим методом получены супрамолекулярные композиции потенциального антигельминтика - артесуната (АРС) с глицирризинатом натрия (Na2ГК) в разных массовых соотношениях компонентов, входящих в комплекс АРС-Na2ГК (1 : 5, 1 : 10 и 1 : 20). Образование комплекса включения АРС с мицеллами Na2ГК подтверждено методами ЯМР-релаксации и ядерного эффекта Оверхаузера (NOESY). Установлено, что комплекс АРС-Na2ГК (1 : 10) обладает оптимальными физико-химическими свойствами для дальнейших биологических исследований на гельминтах Opisthorchis felineus, вызывающих описторхоз у людей и животных. В экспериментах in vitro показано, что комплекс АРС-Na2ГК (1 : 10) оказывает более выраженное угнетение подвижности как у инвазионных особей O. felineus (метацеркарии), так и у половозрелых трематод (мариты) по сравнению с чистым АРС, причем у марит даже при малых концентрациях (0.1-10 мкг/мл). Несмотря на то, что доза АРС в составе АРС-Na2ГК (1 : 10) снижена в 11 раз, показатель эффективности (IC50) был всего в 2.4 раза (для метацеркарий) и 1.45 раза (для марит) меньше, чем у чистого АРС. Полученные результаты свидетельствуют о перспективности использования нового комплекса для дальнейших исследований на O. felineus-инфицированных животных.

Using the mechanochemical method, supramolecular compositions of a potential anthelmintic, artesunate (ARS) with sodium glycyrrhizinate (Na2GA), were obtained in different mass ratios of the components included in ARS-Na2GA complex (1 : 5, 1 : 10, and 1 : 20). The formation of an inclusion complex of ARS with Na2GA micelles was confirmed by NMR relaxation and the nuclear Overhauser effect (NOESY). It has been established that the ARS-Na2GA complex (1 : 10) has optimal physicochemical properties for further biological studies on the helminths Opisthorchis felineus , which cause opisthorchiasis in humans and animals. Experiments in vitro showed that ARS-Na2GA complex (1 : 10) had a more pronounced inhibition of motility both in invasive individuals of O. felineus (metacercariae) and in adult trematodes (maritae), compared with pure ARS, and even at low concentrations (0.1-10 µg/mL) for maritae. Despite the fact that the dose of ARS in the composition of ARS-Na2GA (1:10) was 11 times lower, the efficiency coefficient (IC50) declined 2.4-fold (for metacercariae) and 1.45-fold (for maritae) with respect to pure ARS. The results obtained indicate the availability of using the new complex for further studies on O. felineus -infected animals.
ИЗУЧЕНИЕ ВЫСОКОТЕМПЕРАТУРНОГО ПРОЦЕССА ВЫДЕЛЕНИЯ КИСЛОРОДА ИЗ ОКСИДА СО СТРУКТУРОЙ ДВОЙНОГО ПЕРОВСКИТА СОСТАВА YBACO2O6-D В КВАЗИРАВНОВЕСНОМ РЕЖИМЕ
Authors: М.П. ПОПОВ , М.И. ГОНГОЛА , Р.Д. ГУСЬКОВ , И.В. КОВАЛЕВ , А.П. НЕМУДРЫЙ
Published: November 2024

Приведены результаты по изучению процесса высокотемпературной десорбции кислорода из оксида состава YBaCo2O6-d в квазиравновесном режиме. Описана процедура приготовления оксида твердофазным синтезом, способы и методы его аттестации. Получены данные измерения образца методом квазиравновесного выделения кислорода, позволяющие достоверно описать процессы десорбции кислорода из оксида и вычислить изменение его содержания в оксиде. Определено содержание кислорода в YBaCo2O6-d как непрерывная функция температуры и парциального давления кислорода в диапазонах 600-900 °C и 0.2-10-5 атм соответственно. Проведенные исследования позволяют выявить условия фазовых переходов в оксиде и выделить области стабильности проводящей фазы, что необходимо для разработки катодных материалов твердооксидных топливных элементов.

The results of studying high-temperature oxygen desorption from an YBaCo2O6-δ oxide in the quasi-equilibrium regime are presented. The solid-phase synthesis of the oxide, ways and methods of its characterisation are described. The data obtained by quasi-equilibrium oxygen release technique allowed us to provide a reliable description of oxygen desorption from the oxide and to calculate the change in oxygen content in the oxide. Oxygen content in YBaCo2O6-δ is determined as a continuous function of temperature and the partial pressure of oxygen within the ranges of 600-900 °C and 0.2-10-5 atm, respectively. The data obtained make it possible to identify the conditions of phase transitions in the oxide and determine the stability regions of the conductive phase, which is necessary for the development of cathode materials for solid oxide fuel cells.
РЕДКОЗЕМЕЛЬНЫЕ ЭЛЕМЕНТЫ В БУРЫХ УГЛЯХ АЗЕЙСКОГО МЕСТОРОЖДЕНИЯ ИРКУТСКОГО УГОЛЬНОГО БАССЕЙНА
Authors: С.С. ШАЦКАЯ , И.А. ДЕРЕВЯГИНА , Н.Ф. ГЛАЗЫРИНА , А.В. ГАПОНЬКО
Published: November 2024

Разработана эффективная методика подготовки проб и определения содержания редкоземельных элементов (РЗЭ) в бурых углях Азейского месторождения высокочувствительным методом масс-спектрометрии с индуктивно связанной аргоновой плазмой. Относительные стандартные отклонения при определении РЗЭ полуколичественным и количественным методами не превышают 15 и 8 мас. % соответственно. Суммарное содержание редкоземельных элементов составляет 0.92±0.07 кг/т угля и подтверждено независимым методом спектрофотометрии с арсеназо III.

An efficient procedure for sample preparation and determination of rare earth elements (REE) in the brown coals of the Azeysk by a highly sensitive method, mass spectrometry with inductively coupled argon plasma, has been developed. The relative standard deviations in REE determination by the semi-quantitative and quantitative methods do not exceed 15 and 8 wt%, respectively. The total content of rare earth elements is 0.92 ± 0.07 kg/t of coal, which was verified by an independent spectrophotometric method with аrsenazo III.
Enhancing mental health referrals through AI-enabled chatbots
Authors: Banerjee Padmakali , Puri Astha , Mathur Rohan , Puri Amita , Jogy Ms Saumya , Sindhu Aaravdeep , Pandya Bhavna
Published: November 2024

The increasing prevalence of mental health issues calls for innovative approaches to ensure timely and effective access to mental health services. AI-enabled chatbots offer a promising solution by providing immediate, anonymous, and accessible support. This concept note explores the potential of AI-enabled chatbots to enhance mental health outcomes by increasing referrals to mental health services. Key objectives include improving access, reducing stigma, facilitating early intervention, guiding individuals through available resources, and enhancing follow-up care. The document outlines chatbot features, implementation strategies, expected outcomes, and the methodology for assessing their impact, illustrating their potential to revolutionize mental health care delivery.
ALIMENTAÇÃO PARA UM ENVELHECIMENTO SAUDÁVEL
Authors: Zaniboni Isadora , Dias Konrad Shemeli , Böettge Rosa Carolina , H. A. Schwanke Carla
Published: November 2024

O envelhecimento populacional é uma realidade mundial. No Brasil, a expectativa de vida ao nascer, que era de 45,5 anos, passou para 76,6 anos em 2019. Já a expectativa de vida aos 50 anos passou de 19,1 anos para 30,8 anos, no mesmo período (IBGE). Acompanhando o envelhecimento da população, observa-se, também, o aumento da prevalência de doenças crônicas não transmissíveis (DCNT). A presença de DCNT pode ter um impacto negativo no envelhecimento, pois está associada a desfechos como fragilidade (Ding; Kuha; Murphy, 2017), dependência, diminuição da qualidade de vida e morte (Amarya; Singh; Sabharwal, 2014).
ESTRESSE CRÔNICO E SUA IMPLICAÇÃO NO DECLÍNIO COGNITIVO
Authors: Eduardo de Costa Góes Luiz , Malfussi Horst Pedro , Bromberg Elke
Published: November 2024

O envelhecimento saudável está intimamente relacionado a iniciativas preventivas, ou de manejo, de fatores de risco para doenças neurodegenerativas. Neste contexto, é importante chamar a atenção para um fator potencialmente negativo no contexto do envelhecimento cognitivo: o estresse crônico.
Origen y Desarrollo del neoliberalismo en Costa Rica: su impacto en políticas públicas y efectos de la pandemia del COVID-19 (editores: Nancy Piedra Guillen y Sergio Reuben Soto, CLACSO-UCR, 2023)
Authors: Coto Murillo Paulo E.
Published: November 2024

Esta reseña pone en perspectivas los principales aportes del libro Origen y Desarrollo del neoliberalismo en Costa Rica: su impacto en políticas públicas y efectos de la pandemia del COVID-19. La obra se caracteriza por reunir, a lo largo de sus 10 capítulos, a una serie de académicas y académicos, los cuales, dadas sus diferentes posturas teórico-analíticas, acaba por hacer de este texto un artefacto cultural polifónico, es decir, se conforma como un escenario, en el cual, enfrentan sus posiciones y tesis. Un primer grupo de estudiosas y estudiosos, plantean la necesaria y urgente superación del modelo neoclásico en crisis y, por  el contrario, otro grupo argumenta que el país debe de sobrepasar el modelo  económico proteccionista patriarcal para profundizar y avanzar hacia un  liberalismo político-cultural, así como hacia una economía de libre mercado. Finalmente, el comentario cierra y se posiciona críticamente ante el debate, proponiendo recorridos para reflexiones futuras con respecto al tema y su relación con la neoliberalización de la cultura y las industrias culturales.
Aborto polínico em Tradescantia pallida var. purpurea
Authors: Nogueira Luciana Rodrigues , Stein Isabela Kirch , Duarte Juliana Henrique , Droste Annette
Published: November 2024

Este estudo foi desenvolvido para testar a viabilidade do bioensaio aborto polínico (AP) em botões florais de Tradescantia pallida var. purpurea na avaliação de genotoxicidade da água superficial. Uma nova metodologia foi desenvolvida visando o objetivo de avaliar e comparar diferentes períodos de recuperação para o bioensaio de exposição aguda a água, e encontrar o tempo necessário para que a célula em estádio de tétrade se desenvolva e atinja o estádio de grão de pólen. Diferentes períodos de recuperação foram testados e o tempo de 28h foi suficiente para que as células passassem do estádio de tétrades para grão de pólen. As frequências médias de aborto polínico encontradas neste período foram de 5,07 para a água poluída e de 11,0 e 2,90 para os controles positivo e negativo, respectivamente. Houve relação positiva entre as respostas dos biomarcadores frente à água poluída (r = 0,480; p = 0,028). Os achados deste estudo mostram que o bioensaio de aborto polínico em Tradescantia pallida var. purpurea (Trad-AP) é sensível e eficiente quando comparado ao bioensaio tradicional de micronúcleos (Trad-MCN), podendo ser empregado com êxito no biomonitoramento da água superficial.
Tailored bam shaping techniques for enhanced ultrashort pulse laser processing.
Authors:   , Fantova Jorge
Published: November 2024

Ultrashort pulse (USP) laser processing has set a new standard in the manufacturing industry due to its high precision and its capacity to engrave nearly all materials. This technology has been introduced in sectors such as automotive, aerospace and electronics for tasks such as micro-drilling or surface structuring. However, some challenges to the widespread use of USP laser processing include improving fabrication speed and exibility to meet industrial demands. Beam shaping techniques, which involves altering the amplitude, phase, or polarization of laser beams, have emerged as a convenient solution to address these issues, as they allow for more ecient, versatile, and high-resolution material processing. In this thesis, various beam shaping strategies are explored to boost the throughput and applicability of USP laser manufacturing processes. In particular, the experimental work is divided into three main parts, each focusing on a dierent type of beam shaping. The rst part investigates the optimization and application of Bessel beams for generating volume diraction gratings in dierent glasses. It includes the study of nonlinear laser-matter interactions during irradiation of each material and examines its eect on the morphological and optical properties of the resulting volume modications. By ne-tuning the laser writing parameters, ecient phase elements suitable for multiple applications are produced in all types of glass in a single laser pass. The second part examines the use of triangular beams to streamline asymmetrical surface machining. This novel manufacturing method was employed to engrave grooves with a wide range of geometries in a single step. Additionally, it enabled the straightforward fabrication of blazed gratings on stainless steel, with features similar to those manufactured using conventional methods. The third part focuses on the generation of multilevel at-top beams and its application in USP laser polishing. To optimize light structuring, several diractive optical elements (DOEs) were manufactured using a femtosecond (fs) laser-assisted lithographic process. The results of this line of work demonstrate the importance of the design and fabrication of the DOEs to ensure homogeneous beam shaping and uniform processing using an USP laser source. Overall, this thesis presents new strategies to enhance the precision and eciency of ultrashort pulse (USP) laser material processing. The ndings contribute to the broader eld of laser microfabrication and have clear implications in industrial applications.
CYBERSECURITY IN THE EXCHANGE OF BIOMETRIC AND BIOMEDICAL DATA
Authors: Hristov H. A. , Batalov F. I.
Published: November 2024

Abstract.. Demographic change, such as the rapidly aging populations in many industrialized countries, along with the rise in chronic diseases (such as diabetes and heart disease), has prompted many professionals to explore how technology can alleviate the burden on healthcare workers and provide effective tools for the elderly. In particular, IoT (Internet of Things) technologies can play a key role in helping individuals manage their health issues at home. This has led to the development of "personal health devices" that allow individuals to monitor their health condition at home and share this data with healthcare providers and caregivers.
The integration of "cloud" technologies into medical services is crucial for the large-scale deployment of reliable, high-tech solutions aimed at enabling self-monitoring for a broad user base. However, as modern information and communication technologies become increasingly embedded in healthcare, one of the most critical factors remains the reliability and security of medical, biomedical, and biometric data. From this perspective, ensuring cybersecurity in the deployment of such systems and services is of paramount importance.
Understanding Intrinsic Socioeconomic Biases in Large Language Models
Authors: Arzaghi Mina , Carichon Florian , Farnadi Golnoosh
Published: November 2024

Large Language Models (LLMs) are increasingly integrated into critical decision-making processes, such as loan approvals and visa applications, where inherent biases can lead to discriminatory outcomes. In this paper, we examine the nuanced relationship between demographic attributes and socioeconomic biases in LLMs, a crucial yet understudied area of fairness in LLMs. We introduce a novel dataset of one million English sentences to systematically quantify socioeconomic biases across various demographic groups. Our findings reveal pervasive socioeconomic biases in both established models such as GPT-2 and state-of-the-art models like Llama 2 and Falcon. We demonstrate that these biases are significantly amplified when considering intersectionality, with LLMs exhibiting a remarkable capacity to extract multiple demographic attributes from names and then correlate them with specific socioeconomic biases. This research highlights the urgent necessity for proactive and robust bias mitigation techniques to safeguard against discriminatory outcomes when deploying these powerful models in critical real-world applications. Warning: This paper discusses and contains content that can be offensive or upsetting.
Nothing Comes Without Its World – Practical Challenges of Aligning LLMs to Situated Human Values through RLHF
Authors: Arzberger Anne , Buijsman Stefan , Lupetti Maria Luce , Bozzon Alessandro , Yang Jie
Published: November 2024

Work on value alignment aims to ensure that human values
are respected by AI systems. However, existing approaches
tend to rely on universal framings of human values that obscure
the question of which values the systems should capture
and align with, given the variety of operational situations.
This often results in AI systems that privilege only a
selected few while perpetuating problematic norms grounded
on biases, ultimately causing equity and justice issues. In this
perspective paper, we unpack the limitations of predominant
alignment practices of reinforcement learning from human
feedback (RLHF) for LLMs through the lens of situated values.
We build on feminist epistemology to argue that at the
design-time, RLHF has problems with representation in the
subjects providing feedback and implicitness in the conceptualization
of values and situations of real-world users while
lacking system adaptation to real user situations at the use time.
To address these shortcomings, we propose three research
directions: 1) situated annotation to capture information
about the crowdworker’s and user’s values and judgments
in relation to specific situations at both the design and
use-time, 2) expressive instruction to encode plural values
for instructing LLMs systems at design-time, and 3) reflexive
adaptation to leverage situational knowledge for system
adaption at use-time. We conclude by reflecting on the
practical challenges of pursuing these research directions and
situated value alignment of AI more broadly.
Public Attitudes on Performance for Algorithmic and Human Decision-Makers (Extended Abstract)
Authors: Bansak Kirk , Paulson Elisabeth
Published: November 2024

This study explores public preferences between algorithmic and human decision-makers (DMs) in high-stakes contexts, how these preferences are impacted by performance metrics, and whether the public's evaluation of performance differs when considering algorithmic versus human DMs. Leveraging a conjoint experimental design, respondents (n = 9,000) chose between pairs of DM profiles in two scenarios: pre-trial release decisions and bank loan decisions. DM profiles varied on the DM’s type (human vs. algorithm) and on three metrics—defendant crime rate/loan default rate, false positive rate (FPR) among white defendants/applicants, and FPR among minority defendants/applicants—as well as an implicit (un)fairness metric defined by the absolute difference between the two FPRs. Controlling for performance, we observe a general tendency to favor human DMs, though this is driven by a subset of respondents who expect human DMs to perform better in the real world, and there is an analogous group with the opposite preference for algorithmic DMs. We also find that the relative importance of the four performance metrics remains consistent across DM type, suggesting that the public's preferences related to DM performance do not vary fundamentally between algorithmic and human DMs. Taken together, the results collectively suggest that people have very different beliefs about what type of DM (human or algorithmic) will deliver better performance and should be preferred, but they have similar desires in terms of what they want that performance to be regardless of DM type.
Beyond Thumbs Up/Down: Untangling Challenges of Fine-Grained Feedback for Text-to-Image Generation
Authors: Collins Katherine M. , Kim Najoung , Bitton Yonatan , Rieser Verena , Omidshafiei Shayegan , Hu Yushi , Chen Sherol , Dutta Senjuti , Chang Minsuk , Lee Kimin , Liang Youwei , Evans Georgina , Singla Sahil , Li Gang , Weller Adrian , He Junfeng , Ramachandran Deepak , Dvijotham Krishnamurthy Dj
Published: November 2024

Human feedback plays a critical role in learning and refining reward models for text-to-image generation, but the optimal form the feedback should take for learning an accurate reward function has not been conclusively established. This paper investigates the effectiveness of fine-grained feedback which captures nuanced distinctions in image quality and prompt-alignment, compared to traditional coarse-grained feedback (for example, thumbs up/down or ranking between a set of options). While fine-grained feedback holds promise, particularly for systems catering to diverse societal preferences, we show that demonstrating its superiority to coarse-grained feedback is not automatic. Through experiments on real and synthetic preference data, we surface the complexities of building effective models due to the interplay of model choice, feedback type, and the alignment between human judgment and computational interpretation. We identify key challenges in eliciting and utilizing fine-grained feedback, prompting a reassessment of its assumed benefits and practicality. Our findings -- e.g., that fine-grained feedback can lead to worse models for a fixed budget, in some settings; however, in controlled settings with known attributes, fine grained rewards can indeed be more helpful -- call for careful consideration of feedback attributes and potentially beckon novel modeling approaches to appropriately unlock the potential value of fine-grained feedback in-the-wild.
What Makes An Expert? Reviewing How ML Researchers Define "Expert"
Authors: Diaz Mark , Smith Angela D.R.
Published: November 2024

Human experts are often engaged in the development of machine learning systems to collect and validate data, consult on algorithm development, and evaluate system performance. At the same time, who counts as an ‘expert’ and what constitutes ‘expertise’ is not always explicitly defined. In this work, we review 112 academic publications that explicitly reference ‘expert’ and ‘expertise’ and that describe the development of machine learning (ML) systems to survey how expertise is characterized and the role experts play. We find that expertise is often undefined and forms of knowledge outside of formal education and professional certification are rarely sought, which has implications for the kinds of knowledge that are recognized and legitimized in ML development. Moreover, we find that expert knowledge tends to be utilized in ways focused on mining textbook knowledge, such as through data annotation. We discuss the ways experts are engaged in ML development in relation to deskilling, the social construction of expertise, and implications for responsible AI development. We point to a need for reflection and specificity in justifications of domain expert engagement, both as a matter of documentation and reproducibility, as well as a matter of broadening the range of recognized expertise.
Red-Teaming for Generative AI: Silver Bullet or Security Theater?
Authors: Feffer Michael , Sinha Anusha , Deng Wesley H. , Lipton Zachary C. , Heidari Hoda
Published: November 2024

In response to rising concerns surrounding the safety, security, and trustworthiness of Generative AI (GenAI) models, practitioners and regulators alike have pointed to AI red-teaming as a key component of their strategies for identifying and mitigating these risks. However, despite AI red-teaming’s central role in policy discussions and corporate messaging, significant questions remain about what precisely it means, what role it can play in regulation, and how it relates to conventional red-teaming practices as originally conceived in the field of cybersecurity. In this work, we identify recent cases of red-teaming activities in the AI industry and conduct an extensive survey of relevant research literature to characterize the scope, structure, and criteria for AI red-teaming practices. Our analysis reveals that prior methods and practices of AI red-teaming diverge along several axes, including the purpose of the activity (which is often vague), the artifact under evaluation, the setting in which the activity is conducted (e.g., actors, resources, and methods), and the resulting decisions it informs (e.g., reporting, disclosure, and mitigation). In light of our findings, we argue that while red-teaming may be a valuable big-tent idea for characterizing GenAI harm mitigations, and that industry may effectively apply red-teaming and other strategies behind closed doors to safeguard AI, gestures towards red-teaming (based on public definitions) as a panacea for every possible risk verge on security theater. To move toward a more robust toolbox of evaluations for generative AI, we synthesize our recommendations into a question bank meant to guide and scaffold future AI red-teaming practices.
Contributory Injustice, Epistemic Calcification and the Use of AI Systems in Healthcare
Authors: Hardalupas Mahi
Published: November 2024

AI systems have long been touted as a means to transform the healthcare system and improve service user outcomes. However, these claims frequently ignore the social context that leaves service users subject to epistemic oppression. This paper introduces the term “epistemic calcification” to describe how the use of AI systems leads to our epistemological systems becoming stuck in fixed frameworks for understanding the world. Epistemic calcification leads to contributory injustice as it reduces the ability of healthcare systems to meaningfully consider alternative understandings of people’s health experiences. By analysing examples of algorithmic prognosis and diagnosis, this paper demonstrates the challenges of addressing contributory injustice in AI systems and the need for contestability to focus on more than the AI system and on the underlying epistemologies of AI systems.
Acceptable Use Policies for Foundation Models
Authors: Klyman Kevin
Published: November 2024

As foundation models have accumulated hundreds of millions of users, developers have begun to take steps to prevent harmful types of uses. One salient intervention that foundation model developers adopt is acceptable use policies—legally binding policies that prohibit users from using a model for specific purposes. This paper identifies acceptable use policies from 30 foundation model developers, analyzes the use restrictions they contain, and argues that acceptable use policies are an important lens for understanding the regulation of foundation models. Taken together, developers’ acceptable use policies include 127 distinct use restrictions; the wide variety in the number and type of use restrictions may create fragmentation across the AI supply chain. Companies also employ acceptable use policies to prevent competitors or specific industries from making use of their models. Developers alone decide what constitutes acceptable use, and rarely provide transparency about how they enforce their policies. In practice, acceptable use policies are difficult to enforce, and scrupulous enforcement can act as a barrier to researcher access and limit beneficial uses of foundation models. Acceptable use policies for foundation models are an early example of self-regulation that have a significant impact on the market for foundation models and the AI ecosystem.
Responsible Reporting for Frontier AI Development
Authors: Kolt Noam , Anderljung Markus , Barnhart Joslyn , Brass Asher , Esvelt Kevin , Hadfield Gillian K. , Heim Lennart , Rodriguez Mikel , Sandbrink Jonas B. , Woodside Thomas
Published: November 2024

Mitigating the risks from frontier AI systems requires up-to-date and reliable information about those systems. Organizations that develop and deploy frontier systems have significant access to such information. By reporting safety-critical information to actors in government, industry, and civil society, these organizations could improve visibility into new and emerging risks posed by frontier systems. Equipped with this information, developers could make better informed decisions on risk management, while policymakers could design more targeted and robust regulatory infrastructure. We outline the key features of responsible reporting and propose mechanisms for implementing them in practice.
Human vs. Machine: Behavioral Differences between Expert Humans and Language Models in Wargame Simulations
Authors: Lamparth Max , Corso Anthony , Ganz Jacob , Mastro Oriana Skylar , Schneider Jacquelyn , Trinkunas Harold
Published: November 2024

To some, the advent of artificial intelligence (AI) promises better decision-making and increased military effectiveness while reducing the influence of human error and emotions. However, there is still debate about how AI systems, especially large language models (LLMs) that can be applied to many tasks, behave compared to humans in high-stakes military decision-making scenarios with the potential for increased risks towards escalation and unnecessary conflicts. To test this potential and scrutinize the use of LLMs for such purposes, we use a new wargame experiment with 107 national security experts designed to examine crisis escalation in a fictional US-China scenario and compare the behavior of human player teams to LLM-simulated team responses in separate simulations. Wargames have a long history in the development of military strategy and the response of nations to threats or attacks. Here, we find that the LLM-simulated responses can be more aggressive and significantly affected by changes in the scenario. We show a considerable high-level agreement in the LLM and human responses and significant quantitative and qualitative differences in individual actions and strategic tendencies. These differences depend on intrinsic biases in LLMs regarding the appropriate level of violence following strategic instructions, the choice of LLM, and whether the LLMs are tasked to decide for a team of players directly or first to simulate dialog between a team of players. When simulating the dialog, the discussions lack quality and maintain a farcical harmony. The LLM simulations cannot account for human player characteristics, showing no significant difference even for extreme traits, such as “pacifist” or “aggressive sociopath.” When probing behavioral consistency across individual moves of the simulation, the tested LLMs deviated from each other but generally showed somewhat consistent behavior. Our results motivate policymakers to be cautious before granting autonomy or following AI-based strategy recommendations.
Compute North vs. Compute South: The Uneven Possibilities of Compute-based AI Governance Around the Globe
Authors: Lehdonvirta Vili , Wú Bóxī , Hawkins Zoe
Published: November 2024

Governments have begun to view AI compute infrastructures, including advanced AI chips, as a geostrategic resource. This is partly because “compute governance” is believed to be emerging as an important tool for governing AI systems. In this governance model, states that host AI compute capacity within their territorial jurisdictions are likely to be better placed to impose their rules on AI systems than states that do not. In this study, we provide the first attempt at mapping the global geography of public cloud GPU compute, one particularly important category of AI compute infrastructure. Using a census of hyperscale cloud providers’ cloud regions, we observe that the world is divided into “Compute North” countries that host AI compute relevant for AI development (ie. training), “Compute South” countries whose AI compute is more relevant for AI deployment (ie. running inferencing), and “Compute Desert” countries that host no public cloud AI compute at all. We generate potential explanations for the results using expert interviews, discuss the implications to AI governance and technology geopolitics, and consider possible future trajectories.
Examining the Behavior of LLM Architectures Within the Framework of Standardized National Exams in Brazil
Authors: Locatelli Marcelo Sartori , Miranda Matheus Prado , Da Silva Costa Igor Joaquim , Prates Matheus Torres , Thomé Victor , Zaparoli Monteiro Mateus , Lacerda Tomas , Pagano Adriana , Rios Neto Eduardo , Meira Jr. Wagner , Almeida Virgilio
Published: November 2024

The Exame Nacional do Ensino Médio (ENEM) is a pivotal test for Brazilian students, required for admission to a significant number of universities in Brazil. The test consists of four objective high-school level tests on Math, Humanities, Natural Sciences and Languages, and one writing essay. Students' answers to the test and to the accompanying socioeconomic status questionnaire are made public every year (albeit anonymized) due to transparency policies from the Brazilian Government. In the context of large language models (LLMs), these data lend themselves nicely to comparing different groups of humans with AI, as we can have access to human and machine answer distributions. We leverage these characteristics of the ENEM dataset and compare GPT-3.5 and 4, and MariTalk, a model trained using Portuguese data, to humans, aiming to ascertain how their answers relate to real societal groups and what that may reveal about the model biases. We divide the human groups by using socioeconomic status (SES), and compare their answer distribution with LLMs for each question and for the essay. We find no significant biases when comparing LLM performance to humans on the multiple-choice Brazilian Portuguese tests, as the distance between model and human answers is mostly determined by the human accuracy. A similar conclusion is found by looking at the generated text as, when analyzing the essays, we observe that human and LLM essays differ in a few key factors, one being the choice of words where model essays were easily separable from human ones. The texts also differ syntactically, with LLM generated essays exhibiting, on average, smaller sentences and less thought units, among other differences. These results suggest that, for Brazilian Portuguese in the ENEM context, LLM outputs represent no group of humans, being significantly different from the answers from Brazilian students across all tests. The appendices may be found at https://arxiv.org/abs/2408.05035.
Perception of Experience Influences Altruism and Perception of Agency Influences Trust in Human-Machine Interactions (Extended Abstract)
Authors: Oudah Mayada , Makovi Kinga , Gray Kurt , Battu Balaraju , Rahwan Talal
Published: November 2024

It has been argued that human social and economic interactions depend on the perception of mind of the interacting partner. Minds are perceived along two dimensions: experience, i.e., the ability to feel, and agency, i.e., the ability to act and take responsibility for one’s actions. Here, we pair participants with bots in a dictator game (to measure altruism) and a trust game (to measure trust) while varying the bots’ perceived experience and agency. Here, we pair participants with bots in a dictator game (to measure altruism) and a trust game (to measure trust) while varying the bots' perceived experience and agency. Results demonstrate that the perception of experience influences altruism, while the perception of agency influences trust.
PPS: Personalized Policy Summarization for Explaining Sequential Behavior of Autonomous Agents
Authors: Qian Peizhu , Huang Harrison , Unhelkar Vaibhav
Published: November 2024

AI-enabled agents designed to assist humans are gaining traction in a variety of domains such as healthcare and disaster response. It is evident that, as we move forward, these agents will play increasingly vital roles in our lives. To realize this future successfully and mitigate its unintended consequences, it is imperative that humans have a clear understanding of the agents that they work with. Policy summarization methods help facilitate this understanding by showcasing key examples of agent behaviors to their human users. Yet, existing methods produce “one-size-fits-all” summaries for a generic audience ahead of time. Drawing inspiration from research in pedagogy, we posit that personalized policy summaries can more effectively enhance user understanding. To evaluate this hypothesis, this paper presents and benchmarks a novel technique: Personalized Policy Summarization (PPS). PPS discerns a user’s mental model of the agent through a series of algorithmically generated questions and crafts customized policy summaries to enhance user understanding. Unlike existing methods, PPS actively engages with users to gauge their comprehension of the agent behavior, subsequently generating tailored explanations on the fly. Through a combination of numerical and human subject experiments, we confirm the utility of this personalized approach to explainable AI.
Fairness in Reinforcement Learning: A Survey
Authors: Reuel Anka , Ma Devin
Published: November 2024

While our understanding of fairness in machine learning has significantly progressed, our understanding of fairness in reinforcement learning (RL) remains nascent. Most of the attention has been on fairness in one-shot classification tasks; however, real-world, RL-enabled systems (e.g., autonomous vehicles) are much more complicated in that agents operate in dynamic environments over a long period of time. To ensure the responsible development and deployment of these systems, we must better understand fairness in RL. In this paper, we survey the literature to provide the most up-to-date snapshot of the frontiers of fairness in RL. We start by reviewing where fairness considerations can arise in RL, then discuss the various definitions of fairness in RL that have been put forth thus far. We continue to highlight the methodologies researchers used to implement fairness in single- and multi-agent RL systems and showcase the distinct application domains that fair RL has been investigated in. Finally, we critically examine gaps in the literature, such as understanding fairness in the context of RLHF, that still need to be addressed in future work to truly operationalize fair RL in real-world systems.
Introducing ELLIPS: An Ethics-Centered Approach to Research on LLM-Based Inference of Psychiatric Conditions
Authors: Rocca Roberta , Pistilli Giada , Maheshwari Kritika , Fusaroli Riccardo
Published: November 2024

As mental health care systems worldwide struggle to meet demand, there is increasing focus on using language models (LM) to infer neuropsychiatric conditions or psychopathological traits from language production. Yet, so far, this research has only delivered solutions with limited clinical applicability, due to insufficient consideration of ethical questions crucial to ensuring the synergy between possible applications and model design.
To accelerate progress towards clinically applicable models, our paper charts the ethical landscape of research on language-based inference of psychopathology and provides a practical tool for researchers to navigate it. We identify seven core ethical principles that should guide model development and deployment in this domain, translate them into ELLIPS, an ethical toolkit operationalizing these principles into questions that can guide researchers' choices with respect to data selection, architectures, evaluation, and model deployment, and provide a case study exemplifying its use. With this, we aim to facilitate the emergence of model technology with concrete potential for real-world applicability.
Algorithms and Recidivism: A Multi-disciplinary Systematic Review
Authors: Scaria Arul George , Subramanian Vidya , George Nevin K. , Sengupta Nandana
Published: November 2024

The adoption of algorithms across different jurisdictions have transformed the workings of the criminal justice system, particularly in predicting recidivism risk for bail, sentencing, and parole decisions. This shift from human decision-making to statistical or algorithmic tool-assisted decision-making has prompted discussions regarding the legitimacy of such adoption. Our paper presents the results of a systematic review of the literature on criminal recidivism, spanning both legal and empirical perspectives. By coalescing different approaches, we highlight the most prominent themes that have garnered the attention of researchers so far and some that warrant further investigation.
Trusting Your AI Agent Emotionally and Cognitively: Development and Validation of a Semantic Differential Scale for AI Trust
Authors: Shang Ruoxi , Hsieh Gary , Shah Chirag
Published: November 2024

Trust is not just a cognitive issue but also an emotional one, yet the research in human-AI interactions has primarily focused on the cognitive route of trust development. Recent work has highlighted the importance of studying affective trust towards AI, especially in the context of emerging human-like LLM-powered conversational agents. However, there is a lack of validated and generalizable measures for the two-dimensional construct of trust in AI agents. To address this gap, we developed and validated a set of 27-item semantic differential scales for affective and cognitive trust through a scenario-based survey study. We then further validated and applied the scale through an experiment study. Our empirical findings showed how the emotional and cognitive aspects of trust interact with each other and collectively shape a person's overall trust in AI agents. Our study methodology and findings also provide insights into the capability of the state-of-art LLMs to foster trust through different routes.
Dynamics of Moral Behavior in Heterogeneous Populations of Learning Agents
Authors: Tennant Elizaveta , Hailes Stephen , Musolesi Mirco
Published: November 2024

Growing concerns about safety and alignment of AI systems highlight the importance of embedding moral capabilities in artificial agents: a promising solution is the use of learning from experience, i.e., Reinforcement Learning. In multi-agent (social) environments, complex population-level phenomena may emerge from interactions between individual learning agents. Many of the existing studies rely on simulated social dilemma environments to study the interactions of independent learning agents; however, they tend to ignore the moral heterogeneity that is likely to be present in societies of agents in practice. For example, at different points in time a single learning agent may face opponents who are consequentialist (i.e., focused on maximizing outcomes over time), norm-based (i.e., conforming to specific norms), or virtue-based (i.e., considering a combination of different virtues). The extent to which agents' co-development may be impacted by such moral heterogeneity in populations is not well understood. In this paper, we present a study of the learning dynamics of morally heterogeneous populations interacting in a social dilemma setting. Using an Iterated Prisoner's Dilemma environment with a partner selection mechanism, we investigate the extent to which the prevalence of diverse moral agents in populations affects individual agents' learning behaviors and emergent population-level outcomes. We observe several types of non-trivial interactions between pro-social and anti-social agents, and find that certain types of moral agents are able to steer selfish agents towards more cooperative behavior.
Decoding Multilingual Moral Preferences: Unveiling LLM's Biases through the Moral Machine Experiment
Authors: Vida Karina , Damken Fabian , Lauscher Anne
Published: November 2024

Large language models (LLMs) increasingly find their way into the most diverse areas of our everyday lives. They indirectly influence people's decisions or opinions through their daily use. Therefore, understanding how and which moral judgements these LLMs make is crucial. However, morality is not universal and depends on the cultural background. This raises the question of whether these cultural preferences are also reflected in LLMs when prompted in different languages or whether moral decision-making is consistent across different languages. So far, most research has focused on investigating the inherent values of LLMs in English. While a few works conduct multilingual analyses of moral bias in LLMs in a multilingual setting, these analyses do not go beyond atomic actions. To the best of our knowledge, a multilingual analysis of moral bias in dilemmas has not yet been conducted.

To address this, our paper builds on the moral machine experiment (MME) to investigate the moral preferences of five LLMs, Falcon, Gemini, Llama, GPT, and MPT, in a multilingual setting and compares them with the preferences collected from humans belonging to different cultures. To accomplish this, we generate 6500 scenarios of the MME and prompt the models in ten languages on which action to take. Our analysis reveals that all LLMs inhibit different moral biases to some degree and that they not only differ from the human preferences but also across multiple languages within the models themselves. Moreover, we find that almost all models, particularly Llama 3, divert greatly from human values and, for instance, prefer saving fewer people over saving more.
Operationalizing Content Moderation “Accuracy” in the Digital Services Act
Authors: Wei Johnny Tian-Zheng , Zufall Frederike , Jia Robin
Published: November 2024

The Digital Services Act, recently adopted by the EU, requires social media platforms to report the ``accuracy'' of their automated content moderation systems. The colloquial term is vague, or open-textured---the literal accuracy (number of correct predictions divided by the total) is not suitable for problems with large class imbalance, and the ground truth and dataset to measure accuracy against is unspecified. Without further specification, the regulatory requirement allows for deficient reporting. In this interdisciplinary work, we operationalize ``accuracy'' reporting by refining legal concepts and relating them to technical implementation. We start by elucidating the legislative purpose of the Act to legally justify an interpretation of ``accuracy'' as precision and recall. These metrics remain informative in class imbalanced settings, and reflect the proportional balancing of Fundamental Rights of the EU Charter. We then focus on the estimation of recall, as its naive estimation can incur extremely high annotation costs and disproportionately interfere with the platform's right to conduct business. Through a simulation study, we show that recall can be efficiently estimated using stratified sampling with trained classifiers, and provide concrete recommendations for its application. Finally, we present a case study of recall reporting for a subset of Reddit under the Act. Based on the language in the Act, we identify a number of ways recall could be reported due to underspecification. We report on one possibility using our improved estimator, and discuss the implications and areas for further legal clarification.
LLM Voting: Human Choices and AI Collective Decision-Making
Authors: Yang Joshua C. , Dailisan Damian , Korecki Marcin , Hausladen Carina I. , Helbing Dirk
Published: November 2024

This paper investigates the voting behaviors of Large Language Models (LLMs), specifically GPT-4 and LLaMA-2, their biases, and how they align with human voting patterns. Our methodology involved using a dataset from a human voting experiment to establish a baseline for human preferences and conducting a corresponding experiment with LLM agents. We observed that the choice of voting methods and the presentation order influenced LLM voting outcomes. We found that varying the persona can reduce some of these biases and enhance alignment with human choices. While the Chain-of-Thought approach did not improve prediction accuracy, it has potential for AI explainability in the voting process. We also identified a trade-off between preference diversity and alignment accuracy in LLMs, influenced by different temperature settings. Our findings indicate that LLMs may lead to less diverse collective outcomes and biased assumptions when used in voting scenarios, emphasizing the need for cautious integration of LLMs into democratic processes.