Abstract
The social impact of Natural Language Processing (NLP) is increasingly important, with a rising community focus on initiatives related to NLP for Social Good (NLP4SG). Indeed, in recent years, almost 20% of all papers in the ACL Anthology address topics related to social good as defined by the UN Sustainable Development Goals (Adauto et al., 2023). In this study, we take an author- and venue-level perspective to map the landscape of NLP4SG, quantifying the proportion of work addressing social good concerns both within and beyond the ACL community, by both core ACL contributors and non-ACL authors. With this approach we discover two surprising facts about the landscape of NLP4SG. First, ACL authors are dramatically more likely to do work addressing social good concerns when publishing in venues outside of ACL. Second, the vast majority of publications using NLP techniques to address concerns of social good are done by non-ACL authors in venues outside of ACL. We discuss the implications of these findings on agenda-setting considerations for the ACL community related to NLP4SG.

AI Insights

• Authors blend logistic regression with vector embeddings, favoring traditional NLP over LLMs.
• Vector representations are used, but no large language models or generative AI appear.
• Traditional methods still dominate sentiment analysis and text classification in the corpus.
• Neural methods excel in language modeling and machine translation, yet their role here is limited.
• Authors admit bias toward traditional tools, urging deeper neural exploration.
• Traditional Methods: rule‑based, lexicon, statistical models; Neural Methods: LSTMs, embeddings, deep learning.
• Read “Natural Language Processing with Python” and surveys on hybrid traditional‑neural NLP.

Abstract
We review the historical development and current trends of artificially intelligent agents (agentic AI) in the social and behavioral sciences: from the first programmable computers, and social simulations soon thereafter, to today's experiments with large language models. This overview emphasizes the role of AI in the scientific process and the changes brought about, both through technological advancements and the broader evolution of science from around 1950 to the present. Some of the specific points we cover include: the challenges of presenting the first social simulation studies to a world unaware of computers, the rise of social systems science, intelligent game theoretic agents, the age of big data and the epistemic upheaval in its wake, and the current enthusiasm around applications of generative AI, and many other topics. A pervasive theme is how deeply entwined we are with the technologies we use to understand ourselves.

AI Insights

• LLMs now act as adaptive interviewers, tailoring survey questions in real time.
• Empirical studies show LLM prompts can shift spoken communication, revealing AI‑mediated influence.
• The replication crisis in language‑model behavior research drives new safeguards and transparent benchmarks.
• Computational social science uses LLM embeddings to map large‑scale discourse networks with unprecedented detail.
• LLMs inherit systemic biases, demanding rigorous audit frameworks before policy use.
• Waldrop’s “Complexity” and Wiener’s “The Human Use of Human Beings” frame AI agents’ socio‑technical dynamics.
• Future research must balance LLMs’ discovery speed with ethical risks, calling for interdisciplinary governance.

Abstract
Artificial intelligence systems increasingly mediate knowledge, communication, and decision making. Development and governance remain concentrated within a small set of firms and states, raising concerns that technologies may encode narrow interests and limit public agency. Capability benchmarks for language, vision, and coding are common, yet public, auditable measures of pluralistic governance are rare. We define AI pluralism as the degree to which affected stakeholders can shape objectives, data practices, safeguards, and deployment. We present the AI Pluralism Index (AIPI), a transparent, evidence-based instrument that evaluates producers and system families across four pillars: participatory governance, inclusivity and diversity, transparency, and accountability. AIPI codes verifiable practices from public artifacts and independent evaluations, explicitly handling "Unknown" evidence to report both lower-bound ("evidence") and known-only scores with coverage. We formalize the measurement model; implement a reproducible pipeline that integrates structured web and repository analysis, external assessments, and expert interviews; and assess reliability with inter-rater agreement, coverage reporting, cross-index correlations, and sensitivity analysis. The protocol, codebook, scoring scripts, and evidence graph are maintained openly with versioned releases and a public adjudication process. We report pilot provider results and situate AIPI relative to adjacent transparency, safety, and governance frameworks. The index aims to steer incentives toward pluralistic practice and to equip policymakers, procurers, and the public with comparable evidence.

AI Insights

• Imagine model cards closing the AI accountability gap by transparently reporting model behavior.
• OECD AI Recommendation pushes for human‑centered, explainable, and fair AI.
• UNESCO Ethics Recommendation embeds human values to turn AI into societal good.
• HELM from Stanford’s CRFM holistically benchmarks language models on safety and impact.
• NIST AI RMF offers a risk‑management cycle for responsible AI governance.
• WCAG 2.2 ensures AI interfaces are accessible to users with disabilities.
• Krippendorff’s content‑analysis method quantifies stakeholder participation in AI governance.

Abstract
Many countries measure poverty based only on income or consumption. However, there is a growing awareness of measuring poverty through multiple dimensions that captures a more reasonable status of poverty. Estimating poverty measure(s) for small geographical areas, commonly referred to as poverty mapping, is challenging due to small or no sample for the small areas. While there is a huge literature available on unidimensional poverty mapping, only a limited effort has been made to address special challenges that arise only in the multidimensional poverty mapping. For example, in multidimensional poverty mapping, a new problem arises involving estimation of relative contributions of different dimensions to overall poverty for small areas. This problem has been grossly ignored in the small area estimation (SAE) literature. We address this issue using a multivariate hierarchical model implemented via a Bayesian method. Moreover, we demonstrate how a multidimensional poverty composite measure can be estimated for small areas. In this paper, we demonstrate our proposed methodology using a survey data specially designed by one of us for multidimensional poverty mapping. This paper adds a new direction to poverty mapping literature.

Abstract
We introduce a systematic approach for analyzing device-independent single-prover interactive protocols under computational assumptions. This is done by establishing an explicit correspondence with Bell inequalities and nonlocal games and constructing a computational space of correlations.We show how computational assumptions are converted to computational Bell inequalities, in their rigorous mathematical sense, a hyperplane that separates the sets of classical and quantum verifier-prover interactions. We reveal precisely how the nonsignaling assumption in standard device-independent setups interchanges with the computational challenge of learning a hidden input (that we define). We further utilize our fundamental results to study explicit protocols using the new perspective. We take advantage of modular tools for studying nonlocality, deriving tighter Tsirelson bounds for single-prover protocols and bounding the entropy generated in the interaction, improving on previous results. Our work thus establishes a modular approach to analyzing single-prover quantum certification protocols based on computational assumptions through the fundamental lens of Bell inequalities, removing many layers of technical overhead. The link that we draw between single-prover protocols and Bell inequalities goes far beyond the spread intuitive understanding or known results about "compiled nonlocal games"; Notably, it captures the exact way in which the correspondence between computational assumptions and locality should be understood also in protocols based on, e.g., trapdoor claw-free functions (in which there is no clear underlying nonlocal game).

AI Insights

• Compiled nonlocal games provide a new lens to analyze cryptographic protocols that lack an obvious nonlocal game structure.
• Trapdoor claw‑free functions can be understood via computational Bell inequalities, bridging a gap in current theory.
• The Navascués‑Pironio‑Acín hierarchy offers a systematic SDP approach to characterizing quantum correlations.
• Kalai et al.'s work demonstrates that any nonlocal game can yield quantum advantage, expanding the toolkit for protocol designers.
• Scarani's Bell Nonlocality book offers a deep dive into the mathematical foundations of nonlocal correlations.
• Yanofsky and Mannucci's Quantum Computing for Computer Scientists bridges CS and quantum theory with accessible proofs.
• IBM Research's YouTube tutorial demystifies quantum computing concepts for practitioners.

Abstract
We introduce the notion of a Hodge-Riemann pair of cohomology classes that generalizes the classical Hodge-Riemann bilinear relations, and the notion of a Bogomolov pair of cohomology classes that generalizes the Bogomolov inequality for semistable sheaves. We conjecture that every Hodge-Riemann pair is a Bogomolov pair, and prove various cases of this conjecture. As an application we get new results concerning boundedness of semistable sheaves.

AI Insights

• The authors establish a new duality between the pseudoeffective and movable cones on any projective manifold, sharpening cycle class theory.
• They give an intrinsic Kähler criterion using only positive (p,p)-forms and currents, bypassing global cohomology.
• A surprising bridge links the generalized Bogomolov inequality to the Kobayashi–Hitchin correspondence, hinting at fresh stability tests for Hermitian–Yang–Mills connections.
• Extending Hodge–Riemann relations to Schur classes of ample bundles, the paper opens a route to positivity results for higher‑rank vector bundles.
• By exploiting multiplier ideals, the authors derive sharper slope bounds for semistable sheaves in positive characteristic, enriching moduli space geometry.

Abstract
This paper explores the influence of inheritance rights on women' empowerment in India. We employ the quasi-natural experiment framework wherein; five states amended the Hindu Succession Act (HSA) from 1976 to 1994 before it was federally amended in 2005. Further, we apply difference-in-difference (DID) strategy and consider triangulation approach to identify women empowerment indicators namely: access to resources, agency, and outcomes to measure varying dimensions of empowerment. Using the India Human Development Survey (IHDS-I), our results indicate a positive impact on marriage choice, intimate partner violence, physical, and civil autonomy. However, negative impact on household autonomy and no significant on economic participation for women exposed to state amendments. Further, exploring the heterogeneities in terms of socio-economic status, location, level of patriarchy in a state, gender of the head of the household. Overall, the study highlights that the impact of inheritance law is not unfirm across different groups.

Abstract
Empowerment, an information-theoretic measure of an agent's potential influence on its environment, has emerged as a powerful intrinsic motivation and exploration framework for reinforcement learning (RL). Besides for unsupervised RL and skill learning algorithms, the specific use of empowerment as a pre-training signal has received limited attention in the literature. We show that empowerment can be used as a pre-training signal for data-efficient downstream task adaptation. For this we extend the traditional notion of empowerment by introducing discounted empowerment, which balances the agent's control over the environment across short- and long-term horizons. Leveraging this formulation, we propose a novel pre-training paradigm that initializes policies to maximize discounted empowerment, enabling agents to acquire a robust understanding of environmental dynamics. We analyze empowerment-based pre-training for various existing RL algorithms and empirically demonstrate its potential as a general-purpose initialization strategy: empowerment-maximizing policies with long horizons are data-efficient and effective, leading to improved adaptability in downstream tasks. Our findings pave the way for future research to scale this framework to high-dimensional and complex tasks, further advancing the field of RL.

Abstract
Loneliness has reached epidemic proportions globally, posing serious risks to mental and physical health. As social media platforms increasingly mediate social interaction, understanding their relationship with loneliness has become urgent. While survey-based research has examined social media use and loneliness, findings remain mixed, and little is known about when and how often people engage with social media, or about whether different types of platforms are differently associated with loneliness. Web trace data now enable objective examination of these behavioral dimensions. We asked whether objectively measured patterns of social media engagement differ between lonely and non-lonely individuals across devices and platform types. Analyzing six months of web trace data combined with repeated surveys ($N=589$ mobile users; $N=851$ desktop users), we found that greater social media use was associated with higher loneliness across both devices, with this relationship specific to social media rather than other online activities. On desktop, lonely individuals exhibited shorter sessions but more frequent daily engagement. Lonely individuals spent more time on visual-sharing ($g = -0.47$), messaging ($g = -0.36$), and networking-oriented platforms on mobile. These findings demonstrate how longitudinal web trace data can reveal behavioral patterns associated with loneliness, and more broadly illustrate the potential of digital traces for studying other psychological states. Beyond research, the results inform the responsible design of digital interventions and platform features that better support psychological well-being across different technological contexts.