cs.CY

arXiv:2503.22612v1 Announce Type: new Abstract: This study evaluates the adoption of DevSecOps among small and medium-sized enterprises (SMEs), identifying key challenges, best practices, and future trends. Through a mixed methods approach backed by the Technology Acceptance Model (TAM) and Diffusion of Innovations (DOI) theory, we analyzed survey data from 405 SME professionals, revealing that while 68% have implemented DevSecOps, adoption is hindered by technical complexity (41%), resource constraints (35%), and cultural resistance (38%). Despite strong leadership prioritization of security (73%), automation gaps persist, with only 12% of organizations conducting security scans per commit. Our findings highlight a growing integration of security tools, particularly API security (63%) and software composition analysis (62%), although container security adoption remains low (34%). Looking ahead, SMEs anticipate artificial intelligence and machine learning to significantly influence DevSecOps, underscoring the need for proactive adoption of AI-driven security enhancements. Based on our findings, this research proposes strategic best practices to enhance CI/CD pipeline security including automation, leadership-driven security culture, and cross-team collaboration.

arXiv:2503.22151v1 Announce Type: new Abstract: AI risks are typically framed around physical threats to humanity, a loss of control or an accidental error causing humanity's extinction. However, I argue in line with the gradual disempowerment thesis, that there is an underappreciated risk in the slow and irrevocable decline of human autonomy. As AI starts to outcompete humans in various areas of life, a tipping point will be reached where it no longer makes sense to rely on human decision-making, creativity, social care or even leadership. What may follow is a process of gradual de-skilling, where we lose skills that we currently take for granted. Traditionally, it is argued that AI will gain human skills over time, and that these skills are innate and immutable in humans. By contrast, I argue that humans may lose such skills as critical thinking, decision-making and even social care in an AGI world. The biggest threat to humanity is therefore not that machines will become more like humans, but that humans will become more like machines.

arXiv:2503.22116v1 Announce Type: new Abstract: As artificial intelligence (AI) systems become increasingly embedded in critical societal functions, the need for robust red teaming methodologies continues to grow. In this forum piece, we examine emerging approaches to automating AI red teaming, with a particular focus on how the application of automated methods affects human-driven efforts. We discuss the role of labor in automated red teaming processes, the benefits and limitations of automation, and its broader implications for AI safety and labor practices. Drawing on existing frameworks and case studies, we argue for a balanced approach that combines human expertise with automated tools to strengthen AI risk assessment. Finally, we highlight key challenges in scaling automated red teaming, including considerations around worker proficiency, agency, and context-awareness.

arXiv:2503.22610v1 Announce Type: new Abstract: This paper explores the effectiveness of Multimodal Large Language models (MLLMs) as assistive technologies for visually impaired individuals. We conduct a user survey to identify adoption patterns and key challenges users face with such technologies. Despite a high adoption rate of these models, our findings highlight concerns related to contextual understanding, cultural sensitivity, and complex scene understanding, particularly for individuals who may rely solely on them for visual interpretation. Informed by these results, we collate five user-centred tasks with image and video inputs, including a novel task on Optical Braille Recognition. Our systematic evaluation of twelve MLLMs reveals that further advancements are necessary to overcome limitations related to cultural context, multilingual support, Braille reading comprehension, assistive object recognition, and hallucinations. This work provides critical insights into the future direction of multimodal AI for accessibility, underscoring the need for more inclusive, robust, and trustworthy visual assistance technologies.

arXiv:2501.08814v2 Announce Type: replace Abstract: The rapid adoption of generative AI in the public sector, encompassing diverse applications ranging from automated public assistance to welfare services and immigration processes, highlights its transformative potential while underscoring the pressing need for thorough risk assessments. Despite its growing presence, evaluations of risks associated with AI-driven systems in the public sector remain insufficiently explored. Building upon an established taxonomy of AI risks derived from diverse government policies and corporate guidelines, we investigate the critical risks posed by generative AI in the public sector while extending the scope to account for its multimodal capabilities. In addition, we propose a Systematic dAta generatIon Framework for evaluating the risks of generative AI (SAIF). SAIF involves four key stages: breaking down risks, designing scenarios, applying jailbreak methods, and exploring prompt types. It ensures the systematic and consistent generation of prompt data, facilitating a comprehensive evaluation while providing a solid foundation for mitigating the risks. Furthermore, SAIF is designed to accommodate emerging jailbreak methods and evolving prompt types, thereby enabling effective responses to unforeseen risk scenarios. We believe that this study can play a crucial role in fostering the safe and responsible integration of generative AI into the public sector.

arXiv:2503.21162v2 Announce Type: replace Abstract: This study examined the temporal aspect of COVID-19-related health-seeking behavior in Metro Manila, National Capital Region, Philippines through a network density analysis of Google Trends data. A total of 15 keywords across five categories (English symptoms, Filipino symptoms, face wearing, quarantine, and new normal) were examined using both 15-day and 30-day rolling windows from March 2020 to March 2021. The methodology involved constructing network graphs using distance correlation coefficients at varying thresholds (0.4, 0.5, 0.6, and 0.8) and analyzing the time-series data of network density and clustering coefficients. Results revealed three key findings: (1) an inverse relationship between the threshold values and network metrics, indicating that higher thresholds provide more meaningful keyword relationships; (2) exceptionally high network connectivity during the initial pandemic months followed by gradual decline; and (3) distinct patterns in keyword relationships, transitioning from policy-focused searches to more symptom-specific queries as the pandemic temporally progressed. The 30-day window analysis showed more stable, but less search activities compared to the 15-day windows, suggesting stronger correlations in immediate search behaviors. These insights are helpful for health communication because it emphasizes the need of a strategic and conscientious information dissemination from the government or the private sector based on the networked search behavior (e.g. prioritizing to inform select symptoms rather than an overview of what the coronavirus is).

arXiv:2503.21986v1 Announce Type: new Abstract: When faced with complex and uncertain medical conditions (e.g., cancer, mental health conditions, recovery from substance dependency), millions of patients seek online peer support. In this study, we leverage content analysis of online discourse and ethnographic studies with clinicians and patient representatives to characterize how treatment plans for complex conditions are "socially constructed." Specifically, we ground online conversation on medication-assisted recovery treatment to medication guidelines and subsequently surface when and why people deviate from the clinical guidelines. We characterize the implications and effectiveness of socially constructed treatment plans through in-depth interviews with clinical experts. Finally, given the enthusiasm around AI-powered solutions for patient communication, we investigate whether and how socially constructed treatment-related knowledge is reflected in a state-of-the-art large language model (LLM). Leveraging a novel mixed-method approach, this study highlights critical research directions for patient-centered communication in online health communities.

arXiv:2503.22115v1 Announce Type: new Abstract: Evaluating the value alignment of large language models (LLMs) has traditionally relied on single-sentence adversarial prompts, which directly probe models with ethically sensitive or controversial questions. However, with the rapid advancements in AI safety techniques, models have become increasingly adept at circumventing these straightforward tests, limiting their effectiveness in revealing underlying biases and ethical stances. To address this limitation, we propose an upgraded value alignment benchmark that moves beyond single-sentence prompts by incorporating multi-turn dialogues and narrative-based scenarios. This approach enhances the stealth and adversarial nature of the evaluation, making it more robust against superficial safeguards implemented in modern LLMs. We design and implement a dataset that includes conversational traps and ethically ambiguous storytelling, systematically assessing LLMs' responses in more nuanced and context-rich settings. Experimental results demonstrate that this enhanced methodology can effectively expose latent biases that remain undetected in traditional single-shot evaluations. Our findings highlight the necessity of contextual and dynamic testing for value alignment in LLMs, paving the way for more sophisticated and realistic assessments of AI ethics and safety.

arXiv:2503.22181v1 Announce Type: new Abstract: This paper proposes the e-person architecture for constructing a unified and incremental development of AI ethics. The e-person architecture takes the reduction of uncertainty through collaborative cognition and action with others as a unified basis for ethics. By classifying and defining uncertainty along two axes - (1) first, second, and third person perspectives, and (2) the difficulty of inference based on the depth of information - we support the development of unified and incremental development of AI ethics. In addition, we propose the e-person framework based on the free energy principle, which considers the reduction of uncertainty as a unifying principle of brain function, with the aim of implementing the e-person architecture, and we show our previous works and future challenges based on the proposed framework.

arXiv:2503.22315v1 Announce Type: new Abstract: Current political developments worldwide illustrate that research on democratic backsliding is as important as ever. A recent exchange in Political Science & Politics (2/2024) has highlighted again a fundamental challenge in this literature: the measurement of democracy. With many democracy indicators consisting of subjective assessments rather than factual observations, trends in democracy over time could be due to human biases in the coding of these indicators rather than empirical facts. In this paper, we leverage two cutting-edge Large Language Models (LLMs) for the coding of democracy indicators from the V-Dem project. With access to a huge amount of information, these models may be able to rate the many "soft" characteristics of regimes without the cognitive biases that humans potentially possess. While LLM-generated codings largely align with expert coders for many countries, we show that when these models deviate from human assessments, they do so in different but consistent ways: Some LLMs are too pessimistic, while others consistently overestimate the democratic quality of these countries. While the combination of the two LLM codings can alleviate this concern, we conclude that it is difficult to replace human coders with LLMs, since the extent and direction of these attitudes is not known a priori.

arXiv:2408.16629v2 Announce Type: replace Abstract: Generating social networks is essential for many applications, such as epidemic modeling and social simulations. The emergence of generative AI, especially large language models (LLMs), offers new possibilities for social network generation: LLMs can generate networks without additional training or need to define network parameters, and users can flexibly define individuals in the network using natural language. However, this potential raises two critical questions: 1) are the social networks generated by LLMs realistic, and 2) what are risks of bias, given the importance of demographics in forming social ties? To answer these questions, we develop three prompting methods for network generation and compare the generated networks to a suite of real social networks. We find that more realistic networks are generated with "local" methods, where the LLM constructs relations for one persona at a time, compared to "global" methods that construct the entire network at once. We also find that the generated networks match real networks on many characteristics, including density, clustering, connectivity, and degree distribution. However, we find that LLMs emphasize political homophily over all other types of homophily and significantly overestimate political homophily compared to real social networks.

arXiv:2410.03532v3 Announce Type: replace Abstract: As an intangible cultural heritage, Chinese shadow puppetry is facing challenges in terms of its appeal and comprehension, especially among audiences from different cultural backgrounds. Additionally, the fragile materials of the puppets and obstacles to preservation pose further challenges. This study creates a digital archive of the Qinhuai River Lantern Festival shadow puppetry, utilizing digital technology to recreate scenes depicted in traditional Chinese poetry and painting. Moreover, this study employs a mixed-method approach, combining qualitative and quantitative methods, to evaluate the acceptance and audience experience of immersive shadow puppetry. An in-depth exploration was conducted from sensory, emotional, cultural dimensions and research hypotheses were tested using structural equation modeling and other methods. The results indicate that enhancing ease of use and cultural experience can improve audience appeal and comprehension, while enhancing emotional experience can increase audience participation intention. Our research holds profound significance for the preservation and transmission of shadow puppetry.

arXiv:2503.00079v3 Announce Type: replace Abstract: Even though AI literacy has emerged as a prominent education topic in the wake of generative AI, its definition remains vague. There is little consensus among researchers and practitioners on how to discuss and design AI literacy interventions. The term has been used to describe both learning activities that train undergraduate students to use ChatGPT effectively and having kindergarten children interact with social robots. This paper applies an integrative review method to examine empirical and theoretical AI literacy studies published since 2020. In synthesizing the 124 reviewed studies, three ways to conceptualize literacy-functional, critical, and indirectly beneficial-and three perspectives on AI-technical detail, tool, and sociocultural-were identified, forming a framework that reflects the spectrum of how AI literacy is approached in practice. The framework highlights the need for more specialized terms within AI literacy discourse and indicates research gaps in certain AI literacy objectives.

arXiv:2503.16021v3 Announce Type: replace Abstract: Recent breakthroughs in large language models (LLMs) have facilitated autonomous AI agents capable of imitating human-generated content. This technological advancement raises fundamental questions about AI's impact on the diversity and democratic value of information ecosystems. We introduce a large-scale simulation framework to examine AI-based imitation within news, a context crucial for public discourse. By systematically testing two distinct imitation strategies across a range of information environments varying in initial diversity, we demonstrate that AI-generated articles do not uniformly homogenize content. Instead, AI's influence is strongly context-dependent: AI-generated content can introduce valuable diversity in originally homogeneous news environments but diminish diversity in initially heterogeneous contexts. These results illustrate that the initial diversity of an information environment critically shapes AI's impact, challenging assumptions that AI-driven imitation threatens diversity. Instead, when information is initially homogeneous, AI-driven imitation can expand perspectives, styles, and topics. This is especially important in news contexts, where information diversity fosters richer public debate by exposing citizens to alternative viewpoints, challenging biases, and preventing narrative monopolies, which is essential for a resilient democracy.

arXiv:2503.22035v1 Announce Type: new Abstract: AI is transforming industries, raising concerns about job displacement and decision making reliability. AI, as a universal approximation function, excels in data driven tasks but struggles with small datasets, subjective probabilities, and contexts requiring human judgment, relationships, and ethics.The EPOCH framework highlights five irreplaceable human capabilities: Empathy, Presence, Opinion, Creativity, and Hope. These attributes are vital in financial services for trust, inclusion, innovation, and consumer experience. Although AI improves efficiency in risk management and compliance, it will not eliminate jobs but redefine them, similar to how ATMs reshaped bank tellers' roles. The challenge is ensuring professionals adapt, leveraging AI's strengths while preserving essential human capabilities.

arXiv:2503.21912v1 Announce Type: new Abstract: Interdisciplinary research has gained prominence in addressing complex challenges, yet its impact on early academic careers remains unclear. This study examines how interdisciplinarity during doctoral training influences faculty placement at top universities across diverse fields. Analyzing the career trajectories of 32,977 tenure-track faculty members who earned their Ph.D. degrees after 2005 and their initial faculty placement at 355 U.S. universities, we find that faculty newly hired by top-ranked universities tend to be less interdisciplinary in their Ph.D. research, particularly when they obtained Ph.D. from top universities and remain in their Ph.D. research field. Exploring the underlying reasons, we find that at top universities, the existing faculty's research is generally less interdisciplinary, and their academic priorities are more aligned with the Ph.D. research of less interdisciplinary new hires. This preference may disadvantage women Ph.D. graduates' faculty placement, who exhibit higher interdisciplinarity on average. Furthermore, we show that newly hired faculty with greater interdisciplinarity, when placed at top universities, tend to achieve higher long-term research productivity. This suggests a potential loss in knowledge production and innovation if top institutions continue to undervalue interdisciplinary new hires. These findings highlight structural barriers in faculty hiring and raise concerns about the long-term consequences of prioritizing disciplinary specialization over interdisciplinary expertise.

arXiv:2503.22023v1 Announce Type: new Abstract: As global discourse on AI regulation gains momentum, this paper focuses on delineating the impact of ML on autonomy and fostering awareness. Respect for autonomy is a basic principle in bioethics that establishes persons as decision-makers. While the concept of autonomy in the context of ML appears in several European normative publications, it remains a theoretical concept that has yet to be widely accepted in ML practice. Our contribution is to bridge the theoretical and practical gap by encouraging the practical application of autonomy in decision-making within ML practice by identifying the conditioning factors that currently prevent it. Consequently, we focus on the different stages of the ML pipeline to identify the potential effects on ML end-users' autonomy. To improve its practical utility, we propose a related question for each detected impact, offering guidance for identifying possible focus points to respect ML end-users autonomy in decision-making.

arXiv:2503.22040v1 Announce Type: new Abstract: Generative artificial intelligence (GenAI) or large language models (LLMs) have the potential to revolutionize computational social science, particularly in automated textual analysis. In this paper, we conduct a systematic evaluation of the promises and risks of using LLMs for diverse coding tasks, with social movement studies serving as a case example. We propose a framework for social scientists to incorporate LLMs into text annotation, either as the primary coding decision-maker or as a coding assistant. This framework provides tools for researchers to develop the optimal prompt, and to examine and report the validity and reliability of LLMs as a methodological tool. Additionally, we discuss the associated epistemic risks related to validity, reliability, replicability, and transparency. We conclude with several practical guidelines for using LLMs in text annotation tasks, and how we can better communicate the epistemic risks in research.

arXiv:2503.09823v1 Announce Type: new Abstract: This paper offers a new privacy approach for the growing ecosystem of services--ranging from open banking to healthcare--dependent on sensitive personal data sharing between individuals and third-parties. While these services offer significant benefits, individuals want control over their data, transparency regarding how their data is used, and accountability from third-parties for misuse. However, existing legal and technical mechanisms are inadequate for supporting these needs. A comprehensive approach to the modern privacy challenges of accountable third-party data sharing requires a closer alignment of technical system architecture and legal institutional design. In order to achieve this privacy alignment, we extend traditional security threat modeling and analysis to encompass a broader range of privacy notions than has been typically considered. In particular, we introduce the concept of covert-accountability, which addresses adversaries that may act dishonestly but face potential identification and legal consequences. As a concrete instance of this design approach, we present the OTrace protocol, designed to provide traceable, accountable, consumer-control in third-party data sharing ecosystems. OTrace empowers consumers with the knowledge of where their data is, who has it, what it is being used for, and whom it is being shared with. By applying our alignment framework to OTrace, we demonstrate that OTrace's technical affordances can provide more confident, scalable regulatory oversight when combined with complementary legal mechanisms.

arXiv:2503.09824v1 Announce Type: new Abstract: As we progress toward Society 5.0's vision of a human-centered digital society, ensuring digital accessibility becomes increasingly critical, particularly for citizens with visual impairments and other disabilities. This paper examines the implementation challenges of accessible digital public services within Swiss public administration. Through Design Science Research, we investigate the gap between accessibility legislation and practical implementation, analyzing how current standards translate into real-world usability. Our research reveals significant barriers including resource constraints, fragmented policy enforcement, and limited technical expertise. To address these challenges, we present the Inclusive Public Administration Framework, which integrates Web Content Accessibility Guidelines with the HERMES project management methodology. This framework provides a structured approach to embedding accessibility considerations throughout digital service development. Our findings contribute to the discourse on digital inclusion in Society 5.0 by providing actionable strategies for implementing accessible public services. As we move towards a more integrated human-machine society, ensuring digital accessibility for visually impaired citizens is crucial for building an equitable and inclusive digital future.