Hasil untuk "Social pathology. Social and public welfare. Criminology"

Javier Cembrano, Rupert Freeman, Ulrike Schmidt-Kraepelin et al.

We study budget aggregation under $\ell_1$-utilities, a model for collective decision making in which agents with heterogeneous preferences must allocate a public budget across a set of alternatives. Each agent reports their preferred allocation, and a mechanism selects an allocation. Early work focused on social welfare maximization, which in this setting admits truthful mechanisms, but may underrepresent minority groups, motivating the study of proportional mechanisms. However, the dominant proportionality notion, single-minded proportionality, is weak, as it only constrains outcomes when agents hold extreme preferences. To better understand proportionality and its interaction with welfare and truthfulness, we address three questions. First, how much welfare must be sacrificed to achieve proportionality? We formalize this via the price of proportionality, the best worst-case welfare ratio between a proportional mechanism and Util, the welfare-maximizing mechanism. We introduce a new single-minded proportional and truthful mechanism, UtilProp, and show that it achieves the optimal worst-case ratio. Second, how do proportional mechanisms compare in terms of welfare? We define an instance-wise welfare dominance relation and use it to compare mechanisms from the literature. In particular, we show that UtilProp welfare-dominates all previously known single-minded proportional and truthful mechanisms. Third, can stronger notions of proportionality be achieved without compromising welfare guarantees? We answer this question in the affirmative by studying decomposability and proposing GreedyDecomp, a decomposable mechanism with optimal worst-case welfare ratio. We further show that computing the welfare-dominant decomposable mechanism, UtilDecomp, is NP-hard, and that GreedyDecomp provides a 2-approximation to UtilDecomp in terms of welfare.

Jorn K. Teutloff

We present a comparative docking experiment that aligns human-subject interview data with large language model (LLM)-driven synthetic personas to evaluate fidelity, divergence, and blind spots in AI-enabled simulation. Fifteen early-stage startup founders were interviewed about their hopes and concerns regarding AI-powered validation, and the same protocol was replicated with AI-generated founder and investor personas. A structured thematic synthesis revealed four categories of outcomes: (1) Convergent themes - commitment-based demand signals, black-box trust barriers, and efficiency gains were consistently emphasized across both datasets; (2) Partial overlaps - founders worried about outliers being averaged away and the stress of real customer validation, while synthetic personas highlighted irrational blind spots and framed AI as a psychological buffer; (3) Human-only themes - relational and advocacy value from early customer engagement and skepticism toward moonshot markets; and (4) Synthetic-only themes - amplified false positives and trauma blind spots, where AI may overstate adoption potential by missing negative historical experiences. We interpret this comparative framework as evidence that LLM-driven personas constitute a form of hybrid social simulation: more linguistically expressive and adaptable than traditional rule-based agents, yet bounded by the absence of lived history and relational consequence. Rather than replacing empirical studies, we argue they function as a complementary simulation category - capable of extending hypothesis space, accelerating exploratory validation, and clarifying the boundaries of cognitive realism in computational social science.

Samantha Stedtler, Marianna Leventi

This paper argues that conventional blame practices fall short of capturing the complexity of moral experiences, neglecting power dynamics and discriminatory social practices. It is evident that robots, embodying roles linked to specific social groups, pose a risk of reinforcing stereotypes of how these groups behave or should behave, so they set a normative and descriptive standard. In addition, we argue that faulty robots might create expectations of who is supposed to compensate and repair after their errors, where social groups that are already disadvantaged might be blamed disproportionately if they do not act according to their ascribed roles. This theoretical and empirical gap becomes even more urgent to address as there have been indications of potential carryover effects from Human-Robot Interactions (HRI) to Human-Human Interactions (HHI). We therefore urge roboticists and designers to stay in an ongoing conversation about how social traits are conceptualised and implemented in this technology. We also argue that one solution could be to 'embrace the glitch' and to focus on constructively disrupting practices instead of prioritizing efficiency and smoothness of interaction above everything else. Apart from considering ethical aspects in the design phase of social robots, we see our analysis as a call for more research on the consequences of robot stereotyping and blame attribution.

Andrew Blair, Peggy Gregory, Mary Ellen Foster

Though a goal of HRI is the natural integration of social robots into everyday public spaces, real-world studies still occur mostly within controlled environments with predetermined participants. True public spaces present an environment which is largely unconstrained and unpredictable, frequented by a diverse range of people whose goals can often conflict with those of the robot. When combined with the general unfamiliarity most people have with social robots, this leads to unexpected human-robot interactions in these public spaces that are rarely discussed or detected in other contexts. In this paper, we describe atypical users we observed interacting with our robot, and those who did not, during a three-day pilot deployment within a large working church and visitor attraction. We then discuss theoretical future advances in the field that could address these challenges, as well as immediate practical mitigations and strategies to help improve public space human-robot interactions in the present. This work contributes empirical insights into the dynamics of human-robot interaction in public environments and offers actionable guidance for more effective future deployments for social robot designers.

Nicholas Ryder, Diana Johnson, Samantha Bourton et al.

Kenia Augusta Figueiredo

Yasuko Kawahata

This paper presents an interdisciplinary approach to analyze the emergence and impact of filter bubbles in social phenomena, especially in both digital and offline environments, by applying the concepts of quantum field theory. Filter bubbles tend to occur in digital and offline environments, targeting digital natives with extremely low media literacy and information immunity. In addition, in the aftermath of stealth marketing, fake news, "inspirational marketing," and other forms of stealth marketing that never exist are rampant and can lead to major social disruption and exploitation. These are the causes of various social risks, including declining information literacy and knowledge levels and academic achievement. By exploring quantum mechanical principles such as remote interaction, proximity interaction, Feynman diagrams, and loop diagrams, we aim to gain a better understanding of information dissemination and opinion formation in social contexts. Our model incorporates key parameters such as agents' opinions, interaction probabilities, and flexibility in changing opinions, facilitating the observation of opinion distributions, cluster formation, and polarization under a variety of conditions. The purpose of this paper is to mathematically model the filter bubble phenomenon using the concepts of quantum field theory and to analyze its social consequences. This is a discussion paper and the proposed approach offers an innovative perspective for understanding social phenomena, but its interpretation and application require careful consideration. This paper is partially an attempt to utilize "Generative AI" and was written with educational intent. There are currently no plans for it to become a peer-reviewed paper.

Arman Isajanyan, Artur Shatveryan, David Kocharyan et al.

Social reward as a form of community recognition provides a strong source of motivation for users of online platforms to engage and contribute with content. The recent progress of text-conditioned image synthesis has ushered in a collaborative era where AI empowers users to craft original visual artworks seeking community validation. Nevertheless, assessing these models in the context of collective community preference introduces distinct challenges. Existing evaluation methods predominantly center on limited size user studies guided by image quality and prompt alignment. This work pioneers a paradigm shift, unveiling Social Reward - an innovative reward modeling framework that leverages implicit feedback from social network users engaged in creative editing of generated images. We embark on an extensive journey of dataset curation and refinement, drawing from Picsart: an online visual creation and editing platform, yielding a first million-user-scale dataset of implicit human preferences for user-generated visual art named Picsart Image-Social. Our analysis exposes the shortcomings of current metrics in modeling community creative preference of text-to-image models' outputs, compelling us to introduce a novel predictive model explicitly tailored to address these limitations. Rigorous quantitative experiments and user study show that our Social Reward model aligns better with social popularity than existing metrics. Furthermore, we utilize Social Reward to fine-tune text-to-image models, yielding images that are more favored by not only Social Reward, but also other established metrics. These findings highlight the relevance and effectiveness of Social Reward in assessing community appreciation for AI-generated artworks, establishing a closer alignment with users' creative goals: creating popular visual art. Codes can be accessed at https://github.com/Picsart-AI-Research/Social-Reward

Lambert K Engelbrecht

This second edition in 2023 of Social Work/Maatskaplike Werk offers eight articles with themes centred on social work during the hitherto unknown Covid-19 pandemic, child protection, supervision in different contexts and substance abuse respectively.

Allyson L. Dir, Casey Pederson, Shirin Khazvand et al.

Abstract Background For youth involved in the juvenile justice (JJ) system, caregiver involvement and engagement in the system is crucial for youth development and outcomes of JJ cases; however, there are challenges to establishing positive/productive partnerships between caregivers and JJ representatives. The current project examines perspectives of caregivers and JJ personnel regarding facilitators and barriers to establishing JJ-caregiver partnerships, as well as their perceptions of the use of a caregiver navigator program to support caregivers of system-involved youth. Results are used to inform development of a caregiver navigator program to support caregivers and help them navigate the JJ system. Results Semi-structured interviews were conducted with caregivers of youth involved in JJ (n = 15, 53% White, 93% female), JJ personnel (n = 7, 100% White, 50% female), and JJ family advisory board members (n = 5, 100% Black, 100% female). Caregivers reported varying experiences across intake/arrest, court, and probation processes. Positive experiences were characterized by effective communication and feeling supported by JJ. Negative experiences related to feeling blamed and punished for their child’s system involvement and feeling unsupported. JJ interviews corroborated caregiver sentiments and also illustrated facilitators and barriers to JJ-caregiver partnerships. Both JJ personnel and caregivers endorsed potential benefits of a peer-based caregiver navigator program to provide social, informational, and emotional support. Conclusion Continued work is needed to improve JJ-caregiver partnerships and use of a peer-based navigator program has the potential to address barriers to caregiver engagement in the JJ system.

Binbin Lina, Lei Zoua, Bo Zhao et al.

Social media offers a unique lens to observe large-scale, spatial-temporal patterns of users reactions toward critical events. However, social media use varies across demographics, with younger users being more prevalent compared to older populations. This difference introduces biases in data representativeness, and analysis based on social media without proper adjustment will lead to overlooking the voices of digitally marginalized communities and inaccurate estimations. This study explores solutions to pinpoint and alleviate the demographic biases in social media analysis through a case study estimating the public sentiment about COVID-19 using Twitter data. We analyzed the pandemic-related Twitter data in the U.S. during 2020-2021 to (1) elucidate the uneven social media usage among demographic groups and the disparities of their sentiments toward COVID-19, (2) construct an adjusted public sentiment measurement based on social media, the Sentiment Adjusted by Demographics (SAD) index, to evaluate the spatiotemporal varying public sentiment toward COVID-19. The results show higher proportions of female and adolescent Twitter users expressing negative emotions to COVID-19. The SAD index unveils that the public sentiment toward COVID-19 was most negative in January and February 2020 and most positive in April 2020. Vermont and Wyoming were the most positive and negative states toward COVID-19.

Zhong Zhang, Jian-liang Wu, Cun-quan Qu et al.

The dissemination of information and the development of public opinion are essential elements of most social media platforms and are often described as distinct, man-made occurrences. However, what is often disregarded is the interdependence between these two phenomena. Information dissemination serves as the foundation for the formation of public opinion, while public opinion, in turn, drives the spread of information. In our study, we model the co-evolutionary relationship between information and public opinion on heterogeneous multiplex networks. This model takes into account a minority of individuals with steadfast opinions and a majority of individuals with fluctuating views. Our findings reveal the equilibrium state of public opinion in this model and a linear relationship between mainstream public opinion and extreme individuals. Additionally, we propose a strategy for regulating public opinion by adjusting the positions of extreme groups, which could serve as a basis for implementing health policies influenced by public opinion.

Tsuyoshi Idé, Keerthiram Murugesan, Djallel Bouneffouf et al.

This paper is concerned with online targeted advertising on social networks. The main technical task we address is to estimate the activation probability for user pairs, which quantifies the influence one user may have on another towards purchasing decisions. This is a challenging task because one marketing episode typically involves a multitude of marketing campaigns/strategies of different products for highly diverse customers. In this paper, we propose what we believe is the first tensor-based contextual bandit framework for online targeted advertising. The proposed framework is designed to accommodate any number of feature vectors in the form of multi-mode tensor, thereby enabling to capture the heterogeneity that may exist over user preferences, products, and campaign strategies in a unified manner. To handle inter-dependency of tensor modes, we introduce an online variational algorithm with a mean-field approximation. We empirically confirm that the proposed TensorUCB algorithm achieves a significant improvement in influence maximization tasks over the benchmarks, which is attributable to its capability of capturing the user-product heterogeneity.

Daniel A. Epstein, Fannie Liu, Andrés Monroy-Hernández et al.

The CSCW community has a history of designing, implementing, and evaluating novel social interactions in technology, but the process requires significant technical effort for uncertain value. We discuss the opportunities and applications of "piggyback prototyping", building and evaluating new ideas for social computing on top of existing ones, expanding on its potential to contribute design recommendations. Drawing on about 50 papers which use the method, we critically examine the intellectual and technical benefits it provides, such as ecological validity and leveraging well-tested features, as well as research-product and ethical tensions it imposes, such as limits to customization and violation of participant privacy. We discuss considerations for future researchers deciding whether to use piggyback prototyping and point to new research agendas which can reduce the burden of implementing the method.

Serge Thill, Vicky Charisi, Tony Belpaeme et al.

Understanding and modelling children's cognitive processes and their behaviour in the context of their interaction with robots and social artificial intelligence systems is a fundamental prerequisite for meaningful and effective robot interventions. However, children's development involve complex faculties such as exploration, creativity and curiosity which are challenging to model. Also, often children express themselves in a playful way which is different from a typical adult behaviour. Different children also have different needs, and it remains a challenge in the current state of the art that those of neurodiverse children are under-addressed. With this workshop, we aim to promote a common ground among different disciplines such as developmental sciences, artificial intelligence and social robotics and discuss cutting-edge research in the area of user modelling and adaptive systems for children.

I. V. Ustinova

The article examines the influence of spatial and temporal factors on forming a subtheory of forensic forecasting. The historical analysis of the formation of scientific knowledge about spatial and temporal connections and relationships in the light of the development of the general theory of forensic examination has been carried out. In this regard, the author proposed the term “time correlation of anticipated events” and introduced it into the general time system (from the past to the future) of the time intervals for the occurrence of certain adverse events or phenomena, the so-called precursor events, which by their appearance warn of upcoming events. The author highlights the considerable importance of technological forecasting in organizing human activity since this activity presupposes the future development of various kinds of technologies.

Sergio de la Herrán Ruiz-Mateos

El obligado compromiso de los Estados y las instituciones en la lucha contra la violencia de género y la protección a las víctimas está fuera de toda duda. Los avances normativos en esta materia han incorporado expresas referencias a la sujeción preceptiva al seguimiento, prevención y represión de actos de agresión contra las mujeres. En este sentido, Europa y América van alcanzado un desarrollo normativo y jurisprudencial nada desdeñable. Tanto la Corte Interamericana de Derechos Humanos como el Tribunal Europeo de Derechos han logrado dar pasos importantes en esta línea y han desarrollado el parámetro de diligencia debida reforzada en violencia de género para valorar la responsabilidad estatal en el cumplimiento de sus obligaciones de prevención, represión y reparación.

Joshua Zonca, Anna Folso, Alessandra Sciutti

Taking advice from others requires confidence in their competence. This is important for interaction with peers, but also for collaboration with social robots and artificial agents. Nonetheless, we do not always have access to information about others' competence or performance. In these uncertain environments, do our prior beliefs about the nature and the competence of our interacting partners modulate our willingness to rely on their judgments? In a joint perceptual decision making task, participants made perceptual judgments and observed the simulated estimates of either a human participant, a social humanoid robot or a computer. Then they could modify their estimates based on this feedback. Results show participants' belief about the nature of their partner biased their compliance with its judgments: participants were more influenced by the social robot than human and computer partners. This difference emerged strongly at the very beginning of the task and decreased with repeated exposure to empirical feedback on the partner's responses, disclosing the role of prior beliefs in social influence under uncertainty. Furthermore, the results of our functional task suggest an important difference between human-human and human-robot interaction in the absence of overt socially relevant signal from the partner: the former is modulated by social normative mechanisms, whereas the latter is guided by purely informational mechanisms linked to the perceived competence of the partner.

Diuslene Rodrigues da Silva, Esther Luíza de Souza Lemos, Alfredo Batista

Resumo: O artigo objetiva evidenciar a concepção e ações desenvolvidas no campo da inserção social a partir da realidade sócio-histórica da pós-graduação em Serviço Social na Universidade Estadual do Oeste do Paraná - Unioeste, localizada na tríplice fronteira internacional no Sul do país. Reflete sobre o sentido ético-político do Serviço Social como área de conhecimento e da inserção social incluída como quesito no modelo de avaliação da Capes.

Xin Wang, Antonio D. Sirianni, Shaoting Tang et al.

In recent years, social media has increasingly become an important platform for political campaigns, especially elections. It remains elusive how exactly public discourse is driven by the intricate interplay between individual socio-cognitive biases, dueling campaign efforts, and social media platforms. We examine this complex socio-political process by integrating observed retweet networks from the 2016 political networks with an agent-based model of political opinion formation and network structure. Here we show that the range of political viewpoints individuals are willing to consider is a key determinant in the formation of polarized networks and the emergence of echo chambers. We also find that winning majority support in public discourse is determined by both the effort exerted by campaigns and the relative ideological positioning of opposing campaigns. Our results demonstrate how public discourse and political polarization can be modeled as an interactive process of shifting individual opinions, evolving social networks, and political campaigns.