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Generative AI (GenAI) is playing an increasingly important role in open source software (OSS). Beyond completing code and documentation, GenAI is increasingly involved in issues, pull requests, code reviews, and security reports. Yet, cheaper generation does not mean cheaper review - and the resulting maintenance burden has pushed OSS projects to experiment with GenAI-specific rules in contribution guidelines, security policies, and repository instructions, even including a total ban on AI-assisted contributions. However, governing GenAI in OSS is far more than a ban-or-not question. The responses remain scattered, with neither a shared governance framework in practice nor a systematic understanding in research. Therefore, in this paper, we conduct a multi-stage analysis on various qualitative materials related to GenAI governance retrieved from 67 highly visible OSS projects. Our analysis identifies recurring concerns across contribution workflows, derives three governance orientations, and maps out 12 governance strategies and their implementation patterns. We show that governing GenAI in OSS extends well beyond banning - it requires coordinated responses across accountability, v

Online platforms rely on moderation interventions to curb harmful behavior such as hate speech, toxicity, and the spread of mis- and disinformation. Yet research on the effects and possible biases of such interventions faces multiple limitations. For example, existing works frequently focus on single or a few interventions, due to the absence of comprehensive datasets. As a result, researchers must typically collect the necessary data for each new study, which limits opportunities for systematic comparisons. To overcome these challenges, we introduce The Big Ban Theory (TBBT) -- a large dataset of moderation interventions. TBBT covers 25 interventions of varying type, severity, and scope, comprising in total over 339K users and nearly 39M posted messages on Reddit and Voat. For each intervention, we provide standardized metadata and pseudonymized user activity collected three months before and after its enforcement, enabling consistent and comparable analyses of intervention effects. In addition, we provide a descriptive exploratory analysis of the dataset, along with several use cases of how it can support research on content moderation. With this dataset, we aim to support resear

This study assesses the impact of tobacco billboard bans on smoking in Switzerland, exploiting their staggered adoption across regions, i.e., the cantons. Based on retrospective smoking histories from the Swiss Health Survey, a panel of individuals' annual smoking status is reconstructed, containing more than one million observations from 1993 to 2017. Estimation relies on staggered difference-in-differences as well as a complementary latent factor model, which relaxes the common trends assumption. The findings indicate that tobacco billboard bans lead to a reduction in smoking rates. Reductions of up to 0.9 percentage points correspond to an approximate 3% decline in the smoking rate. The effect is driven by women and individuals aged 25-44 and 65+. Overall, this evidence suggests that even partial tobacco advertising bans, such as billboard bans, can effectively reduce smoking rates and serve as a valuable policy tool within comprehensive tobacco prevention strategies.

Do e-scooter speed governance policies yield behavioral safety gains beyond the mechanical cap they impose? A firmware ceiling mechanically prevents speeding, but whether the same riders also generate fewer harsh accelerations and harsh decelerations when the ungoverned mode is withdrawn remains open. We analyze 19.5 million GPS-instrumented trips from 52 South Korean cities (February to November 2023). Our two-stage predict-then-validate design targets two trip-level binary outcomes, any harsh-acceleration event and any harsh-deceleration event. In Phase~I, we predict each outcome's within-user reduction under an ungoverned-to-governed substitution, using a rider-heterogeneous random-parameters binary logit on the pre-ban period. In Phase~II, we validate these predictions using a difference-in-differences specification that exploits the operator's system-wide December~2023 removal of the ungoverned mode. The causal estimates confirm the Phase~I predictions in sign and order of magnitude on both outcomes, are Bonferroni-significant, and satisfy a 3-month pre-ban parallel-trends test. A within-user composition check finds no behavioral offsetting, indicating that firmware removal of

Digital technologies are now central to children's learning, play, communication, identity formation, and social participation. Yet dominant approaches to children's online safety often rely on containment mechanisms, including bans, age gates, parental controls, monitoring, and screen-time restrictions. These approaches can be useful in specific contexts, but they often frame child protection primarily as a problem of restricting access to systems designed for adults. In this paper, we argue that this framing is inadequate for children's digital lives and insufficient as a security paradigm. We propose Child-fit security, a design paradigm in which technologies likely to be used by children treat a child as legitimate users, not attackers to be excluded, vulnerabilities to be patched, or risks to be managed. In this paradigm, children's wellbeing, development, privacy, safety, agency, and rights become core security requirements. This shifts the focus of protection from apps, accounts, and data to the child-system relationship, which means protecting both the child and their participation. We conceptualise child-fit security, contrast it with containment-oriented approaches, defin

We investigate how banning generative artificial intelligence-generated content (AIGC) affects knowledge seeking, knowledge contribution, and contribution efficiency in online question-and-answer communities. After the launch of ChatGPT in late November 2022, several Stack Exchange communities implemented official bans on AIGC over concerns such as less reliable and socially engaged content. Leveraging data from the full network of Stack Exchange communities, we employ a difference-in-differences (DID) approach to examine the impacts of these bans. Our results reveal a double-edged impact: while the AIGC ban increases knowledge seeking, as evidenced by a higher volume of posted questions, it simultaneously reduces contribution efficiency, reflected in a lower proportion of questions receiving satisfactory answers within the expected time frame. Notably, these impacts are only evident in non-STEM communities. We take a socio-technical perspective to explore information reliability and social interactivity as two plausible underlying factors driving the observed changes. Our mechanism exploration reveals that the AIGC ban spurs question volume in topics where AIGC is less reliable an

The internet, once celebrated as a decentralized public sphere, is increasingly undermined by practices such as generative search and shadow banning, which divert traffic and suppress visibility. Generative search, powered by Retrieval Augmented Generation RAG, synthesizes content into direct answers, bypassing websites and depriving them of traffic and revenue. This threatens the sustainability of independent content creators, small enterprises, and the open web ecosystem. Shadow banning, a practice that intentionally reduces the visibility of social media posts through algorithmic moderation, exacerbates these issues by chilling free expression and limiting transparency and accountability. This article explores these opaque practices through a legal and regulatory lens. The first part examines the rise of generative search, analyzing its technological and legal implications, including copyright infringement, unfair competition, and unjust enrichment. It also evaluates potential solutions such as licensing agreements and agentic AI. The second part focuses on shadow banning: algorithmic dissuasion, de-ranking, and the reduction of traffic, with specific attention to Chinas Regulat

A previous study reported that E-Prime (English without the verb "to be") selectively altered reasoning in language models, with cross-model correlations suggesting a structural signature tied to which vocabulary was removed. I designed a replication with active controls to test the proposed mechanism: cognitive restructuring through specific vocabulary-cognition mappings. The experiment tested five conditions (unconstrained control, E-Prime, No-Have, elaborated metacognitive prompt, neutral filler-word ban) across six models and seven reasoning tasks (N=15,600 trials, 11,919 after compliance filtering). Every prediction from the cognitive restructuring hypothesis was disconfirmed. All four treatments outperformed the control (83.0%), including both active controls predicted to show null effects. The neutral filler-word ban, banning words like "very" and "just" with no role in logical inference, produced the largest improvement (+6.7 pp), while E-Prime produced the smallest (+3.7 pp). The four conditions ranked in perfect inverse order of theoretical depth. The cross-model correlation signature did not replicate (mean r=0.005). These results are consistent with a simpler mechanism:

Long-distance mobility sustainability, high-speed railways (HSR) decarbonization effect, and bans for short-haul flights are debated in Europe. Yet, holistic environmental assessments on these topics are scarce. A comparative life cycle assessment (LCA) was conducted on the Paris-Bordeaux transportation options in France: HSR, plane, coach, personal car, and carpooling. The overall ranking on four environmental indicators, from best to worst, is as follows: coach, HSR, carpooling, private car, and plane. Scenario analyses showed that increasing train occupancy decreases the environmental impact of the mode (-12%), while decreasing speed does not. Moreover, worldwide carbon footprints of electric HSR modes range 30-120 gCO2eq per passenger-kilometer traveled. Finally, a consequential LCA highlighted carbon paybacks of the HSR project. Under a business-as-usual trip substitution scenario, the HSR gets net-zero 60 years after construction. With a short-haul flight ban, it occurs after 10 years. This advocates for generalizing short-haul flight bans and investing in HSR infrastructure.

Multi-modal generative AI models integrated into wearable devices have shown significant promise in enhancing the accessibility of visual information for blind or visually impaired (BVI) individuals, as evidenced by the rapid uptake of Meta Ray-Bans among BVI users. However, the proprietary nature of these platforms hinders disability-led innovation of visual accessibility technologies. For instance, OpenAI showcased the potential of live, multi-modal AI as an accessibility resource in 2024, yet none of the presented applications have reached BVI users, despite the technology being available since then. To promote the democratization of visual access technology development, we introduce WhatsAI, a prototype extensible framework that empowers BVI enthusiasts to leverage Meta Ray-Bans to create personalized wearable visual accessibility technologies. Our system is the first to offer a fully hackable template that integrates with WhatsApp, facilitating robust Accessible Artificial Intelligence Implementations (AAII) that enable blind users to conduct essential visual assistance tasks, such as real-time scene description, object detection, and Optical Character Recognition (OCR), utili

For nearly two decades after 2001, Afghanistan's higher education sector expanded rapidly, with Kabul University serving as a central site of women's academic participation. Drawing on administrative records of student populations from 2016-2019 (Islamic calendar 1395-1398), this study examines gender distributions across shifts, faculties, and departments, with particular attention to STEM versus non-STEM fields. At Kabul University, the morning shift refers to the main daytime cohort (including some midday classes), while the evening shift is a separate program with its own classes and students; the two cohorts are administratively and academically distinct. Results show steady growth in the overall female student population, but with marked disparities between morning and evening shifts. Women were concentrated in non-STEM and "socially acceptable" disciplines such as literature, law, and psychology, while within STEM they were relatively well represented in the life sciences but remained significantly underrepresented in technical fields such as engineering, computer science, and physics. Gender parity improved modestly across most faculties, yet the Gender Parity Index (GPI) r

This paper studies the impact of the 2022 Dobbs decision and subsequent state level abortion bans on the labor supply of young women (ages 18-24). Using monthly CPS micro data from January 2021 to December 2023, I exploit cross state variation in post Dobbs abortion policy and estimate Difference-in-Differences (DiD) and Triple-Difference (DDD) models. In a simple DiD comparing young women in ban versus protected states, labor force participation in ban states rises by 3.6 percentage points, while participation among young men in the same states falls by 2.9 percentage points, suggesting that the female response is unlikely to be driven by stronger local labor demand. The preferred DDD specification with state-by-month and gender interacted fixed effects implies a 6.6 percentage point increase in labor force participation for young women in ban states relative to young men. School enrollment does not change significantly, whereas employment increases by about 3 percentage points. These results suggest that abortion bans are associated with an immediate increase in young women's labor market attachment, potentially shifting their short run focus toward current earnings rather than h

Sparse Mixture-of-Experts (MoE) has become a key architecture for scaling large language models (LLMs) efficiently. Recent fine-grained MoE designs introduce hundreds of experts per layer, with multiple experts activated per token, enabling stronger specialization. However, during pre-training, routers are optimized mainly for stability and robustness: they converge prematurely and enforce balanced usage, limiting the full potential of model performance and efficiency at inference. In this work, we uncover two overlooked issues: (i) a few highly influential experts are underutilized due to premature and balanced routing decisions; and (ii) enforcing a fixed number of active experts per token introduces substantial redundancy. Instead of retraining models or redesigning MoE architectures, we introduce Ban&Pick, a post-training, plug-and-play strategy for smarter routing. Pick discovers and reinforces key experts-a small group with outsized impact on performance-leading to notable accuracy gains across domains. Ban further dynamically prunes redundant experts based on layer and token sensitivity, delivering faster inference with minimal accuracy loss. Experiments on fine-grained

Social media platforms have transformed global communication and interaction, with TikTok emerging as a critical tool for education, connection, and social impact, including in contexts where infrastructural resources are limited. Amid growing political discussions about banning platforms like TikTok, such actions can create significant ripple effects, particularly impacting marginalized communities. We present a study on Nepal, where a TikTok ban was recently imposed and lifted. As a low-resource country in transition where digital communication is rapidly evolving, TikTok enables a space for community engagement and cultural expression. In this context, we conducted an online survey (N=108) to explore user values, experiences, and strategies for navigating online spaces post-ban. By examining these transitions, we aim to improve our understanding of how digital technologies, policy responses, and cultural dynamics interact globally and their implications for governance and societal norms. Our results indicate that users express skepticism toward platform bans but often passively accept them without active opposition. Findings suggest the importance of institutionalizing collectiv

This paper examines how adverse supply-side shocks in domestic input markets influence firms' vertical outward foreign direct investment (OFDI) decisions. While the theoretical basis for cost-driven OFDI is well established, empirical evidence on the causal mechanisms remains limited. We develop a framework in which input cost shocks raise unit production costs, but firms undertake vertical OFDI only when shocks are sufficiently severe or when baseline costs are already high. Firm heterogeneity leads to a sorting pattern, whereby more productive firms are more likely to invest abroad. To test this mechanism, we exploit China's 2017 waste paper import ban as an exogenous shock and leverage a distinctive feature of the paper product industry's supply chain. Using a difference-in-differences strategy and firm-level data from 2000 to 2023, we find that the policy shock increased the probability of vertical OFDI by approximately 16% in the post-policy period relative to a control group. These results provide robust evidence that firms respond to domestic input shocks by reallocating production across borders, highlighting vertical OFDI as a strategic response to supply-side disruptions.

Let $G$ be a graph and let $\{X_0,X_1\}$ be a partition of $V(G)$. This partition is called external or unfriendly if every $x \in X_i$ has at least as many neighbours in $X_{1-i}$ as in $X_i$. Every maximum edge-cut gives rise to an external partition, so these partitions are always guaranteed to exist. However, it remains a challenge to find such partitions with additional restrictions. Ban and Linial have conjectured that in the case when $G$ is cubic, there always exists an external partition $\{X_0,X_1\}$ for which $-2 \le |X_0| - |X_1| \le 2$. We prove this in two special cases: whenever $G$ can be decomposed into a cycle and a tree, and whenever $G$ has a cubic tree $T$ for which $G - E(T)$ is bipartite.

Drawing inspiration from neurosciences, artificial neural networks (ANNs) have evolved from shallow architectures to highly complex, deep structures, yielding exceptional performance in auditory recognition tasks. However, traditional ANNs often struggle to align with brain regions due to their excessive depth and lack of biologically realistic features, like recurrent connection. To address this, a brain-like auditory network (BAN) is introduced, which incorporates four neuroanatomically mapped areas and recurrent connection, guided by a novel metric called the brain-like auditory score (BAS). BAS serves as a benchmark for evaluating the similarity between BAN and human auditory recognition pathway. We further propose that specific areas in the cerebral cortex, mainly the middle and medial superior temporal (T2/T3) areas, correspond to the designed network structure, drawing parallels with the brain's auditory perception pathway. Our findings suggest that the neuroanatomical similarity in the cortex and auditory classification abilities of the ANN are well-aligned. In addition to delivering excellent performance on a music genre classification task, the BAN demonstrates a high BAS

The environmental impact of Bitcoin mining has become a significant concern, prompting several governments to consider or implement bans on cryptocurrency mining. However, these well-intentioned policies may lead to unintended consequences, notably the redirection of mining activities to regions with higher carbon intensities. This study aims to quantify the environmental effectiveness of Bitcoin mining bans by estimating the resultant carbon emissions from displaced mining operations. Our findings indicate that, contrary to policy goals, Bitcoin mining bans in low-emission countries can result in a net increase in global carbon emissions, a form of aggravated carbon leakage. We further explore the policy implications of these results, suggesting that more nuanced approaches may be required to mitigate the environmental impact of cryptocurrency mining effectively. This research contributes to the broader discourse on sustainable cryptocurrency regulation and provides a data-driven foundation for evaluating the true environmental costs of Bitcoin regulatory policies.