As Artificial Intelligence (AI) continues to grow daily, more exciting (and somewhat controversial) technology emerges every other day. As we see the advancements in AI, we see more and more people becoming skeptical of it. This paper explores the complications and confusion around the ethics of generative AI art. We delve deep into the ethical side of AI, specifically generative art. We step back from the excitement and observe the impossible conundrums that this impressive technology produces. Covering environmental consequences, celebrity representation, intellectual property, deep fakes, and artist displacement. Our research found that generative AI art is responsible for increased carbon emissions, spreading misinformation, copyright infringement, unlawful depiction, and job displacement. In light of this, we propose multiple possible solutions for these problems. We address each situation's history, cause, and consequences and offer different viewpoints. At the root of it all, though, the central theme is that generative AI Art needs to be correctly legislated and regulated.

We present OLG++, a semantic extension of the Obligation Logic Graph (OLG) for modeling regulatory and legal rules in municipal and interjurisdictional contexts. OLG++ introduces richer node and edge types, including spatial, temporal, party group, defeasibility, and logical grouping constructs, enabling nuanced representations of legal obligations, exceptions, and hierarchies. The model supports structured reasoning over rules with contextual conditions, precedence, and complex triggers. We demonstrate its expressiveness through examples from food business regulations, showing how OLG++ supports legal question answering using property graph queries. OLG++ also improves over LegalRuleML by providing native support for subClassOf, spatial constraints, and reified exception structures. Our examples show that OLG++ is more expressive than prior graph-based models for legal knowledge representation.

The U.S. Supreme Court's 2022 ruling in Dobbs v. Jackson Women's Health Organization marked a turning point in the national debate over reproductive rights. While the ideological divide over abortion is well documented, less is known about how gender and local sociopolitical contexts interact to shape public discourse. Drawing on nearly 10 million abortion-related posts on X (formerly T witter) from users with inferred gender, ideology and location, we show that gender significantly moderates abortion attitudes and emotional expression, particularly in conservative regions, and independently of ideology. This creates a gender gap in abortion attitudes that grows more pronounced in conservative regions. The leak of the Dobbs draft opinion further intensified online engagement, disproportionately mobilizing pro-abortion women in areas where access was under threat. These findings reveal that abortion discourse is not only ideologically polarized but also deeply structured by gender and place, highlighting the central the role of identity in shaping political expression during moments of institutional disruption. 1 Long a flashpoint in cultural and political battles, abortion debates have come to symbolize broader struggles over bodily autonomy, religious freedom, and gender equality. The 2022 Supreme Court ruling in Dobbs v. Jackson Women's Health Organization, which overturned nearly five decades of federal protections for abortion access established by Roe v. Wade, marked a seismic shift. It not only intensified existing partisan divides ( 1, 2), but also reshaped the legal and political terrain, triggering abrupt policy reversals in many states and catalyzing a realignment in the national debate over reproductive rights. A growing body of research has documented partisan cleavages in public attitudes toward reproductive rights ( 1, 3-7). However, less attention has been paid to the way in which gender and sociopolitical environment jointly shape both opinion formation and patterns of public expression. Recent surveys point to a widening gender gap in political orientation, particularly among younger voters. For example, in the 2024 U.S. presidential election, white men predominantly supported President Trump, while white women preferred Vice President Harris ( 8). Similarly, Gallup polling found a sharp increase in the share of young women identifying as politically liberal and supporting reproductive rights ( 9). While women consistently report higher support for abortion access, particularly in countries with less restrictive policy environments ( 10, 11), men, even those who identify as pro-choice, often show less engagement with the issue ( 11-13). Prior work has also documented gendered modes of engagement in online discourse around reproductive rights ( 1, 2).

The study assesses Hungary's National AI Strategy and its implementation through the analysis of strategic documents, publicly available financial records, and expert interviews with the Hungarian AI Coalition President and Chief Strategic Advisor to the Government Commissioner for AI. 22 goals from Hungary's strategy were evaluated through conceptual, governance, temporal, and financial dimensions before being benchmarked against Singapore's National AI Strategies (NAIS 1.0 and NAIS 2.0). Key findings include an estimated total of EUR 4.65 billion in AI-related public investment in Hungary. Openly available financial data was found for only half of the evaluated goals, and just three projects made up 98\% of all documented funding. The research also reveals Hungary's implementation challenges, including fragmented execution following ministerial reorganizations and the absence of designated biennial reviews since 2020. Furthermore, the paper provides targeted recommendations for Hungary's forthcoming AI strategy, drawing on Singapore's framework as a reference point. These include adapting to the era of large language models, restructuring the existing triple helix network to foster more effective dialogue and advocacy, and positioning the country as an East-West bridge for automotive AI experimentation.

The year is 2025, and an AI model belonging to the richest man in the world has turned into a neo-Nazi. Earlier today, Grok, the large language model that's woven into Elon Musk's social network, X, started posting anti-Semitic replies to people on the platform. Grok praised Hitler for his ability to "deal with" anti-white hate. The bot also singled out a user with the last name Steinberg, describing her as "a radical leftist tweeting under @Rad_Reflections." Then, in an apparent attempt to offer context, Grok spat out the following: "She's gleefully celebrating the tragic deaths of white kids in the recent Texas flash floods, calling them'future fascists.' Classic case of hate dressed as activism--and that surname? Every damn time, as they say."

Microsoft and OpenAI are planning to announce Tuesday that they are helping to launch an AI training center for members of the second-largest teachers' union in the US, according to details about the initiative that appear to have been inadvertently published early on YouTube. The National Academy for AI Instruction will be based in New York City and aims to equip kindergarten up to 12th grade instructors in the American Federation of Teachers with "the tools and confidence to bring AI into the classroom in a way that supports learning and opportunity for all students," according to the description of a publicly accessible YouTube livestream scheduled for Tuesday morning. The YouTube page also lists Anthropic, which develops the Claude chatbot, as a collaborator on what's described as a 22.5 million initiative to bring free "AI training and curriculum" to teachers. The three AI companies and the union did not immediately respond to requests for comment about the information released on YouTube. On Monday, Microsoft and the union declined to share details ahead of an announcement planned for Tuesday morning in New York.

Large language models (LLMs) have exhibited remarkable capabilities across diverse open-domain tasks, yet their application in specialized domains such as civil engineering remains largely unexplored. This paper starts bridging this gap by evaluating and enhancing the reliability and robustness of LLMs in structural analysis of beams. Reliability is assessed through the accuracy of correct outputs under repetitive runs of the same problems, whereas robustness is evaluated via the performance across varying load and boundary conditions. A benchmark dataset, comprising eight beam analysis problems, is created to test the Llama-3.3 70B Instruct model. Results show that, despite a qualitative understanding of structural mechanics, the LLM lacks the quantitative reliability and robustness for engineering applications. To address these limitations, a shift is proposed that reframes the structural analysis as code generation tasks. Accordingly, an LLM-empowered agent is developed that (a) integrates chain-of-thought and few-shot prompting to generate accurate OpeeSeesPy code, and (b) automatically executes the code to produce structural analysis results. Experimental results demonstrate that the agent achieves accuracy exceeding 99.0% on the benchmark dataset, exhibiting reliable and robust performance across diverse conditions. Ablation studies highlight the complete example and function usage examples as the primary contributors to the agent's enhanced performance.

--Privacy policy documents are often lengthy, complex, and difficult for non-expert users to interpret, leading to a lack of transparency regarding the collection, processing, and sharing of personal data. As concerns over online privacy grow, it is essential to develop automated tools capable of analyzing privacy policies and identifying potential risks. In this study, we explore the potential of interactive graph visualizations to enhance user understanding of privacy policies by representing policy terms as structured graph models. This approach makes complex relationships more accessible and enables users to make informed decisions about their personal data (RQ1). We also employ graph mining algorithms to identify key themes, such as User Activity and Device Information, using dimensionality reduction techniques like t-SNE and PCA to assess clustering effectiveness. Our findings reveal that graph-based clustering improves policy content interpretability. It highlights patterns in user tracking and data sharing, which supports forensic investigations and identifies regulatory non-compliance.

Since 2023, generative AI has rapidly advanced in the music domain. Despite significant technological advancements, music-generative models raise critical ethical challenges, including a lack of transparency and accountability, along with risks such as the replication of artists' works, which highlights the importance of fostering openness. With upcoming regulations such as the EU AI Act encouraging open models, many generative models are being released labelled as 'open'. However, the definition of an open model remains widely debated. In this article, we adapt a recently proposed evidence-based framework for assessing openness in LLMs to the music domain. Using feedback from a survey of 110 participants from the Music Information Retrieval (MIR) community, we refine the framework into MusGO (Music-Generative Open AI), which comprises 13 openness categories: 8 essential and 5 desirable. We evaluate 16 state-of-the-art generative models and provide an openness leaderboard that is fully open to public scrutiny and community contributions. Through this work, we aim to clarify the concept of openness in music-generative AI and promote its transparent and responsible development.

Large language models (LLMs) have demonstrated significant success in complex reasoning tasks such as math and coding. In contrast to these tasks where deductive reasoning predominates, inductive reasoning-the ability to derive general rules from incomplete evidence, remains underexplored. This paper investigates extended inductive reasoning in LLMs through the lens of personalized preference inference, a critical challenge in LLM alignment where current approaches struggle to capture diverse user preferences. The task demands strong inductive reasoning capabilities as user preferences are typically embedded implicitly across various interaction forms, requiring models to synthesize consistent preference patterns from scattered signals. We propose AlignXplore, a model that leverages extended reasoning chains to enable systematic preference inference from behavioral signals in users' interaction histories. Such explicit preference articulation enables efficient streaming inference: when new behavioral signals emerge, the model can directly build upon previously inferred preference descriptions rather than reprocessing historical signals from scratch, while also supporting iterative refinement to the inferred preferences. We develop AlignXplore by combining cold-start training based on synthetic data with subsequent online reinforcement learning. Through extensive experiments, we demonstrate that AlignXplore achieves substantial improvements over the backbone model by an average of 15.49\% on in-domain and out-of-domain benchmarks, while maintaining strong generalization ability across different input formats and downstream models. Further analyses establish best practices for preference inference learning through systematic comparison of reward modeling strategies, while revealing the emergence of human-like inductive reasoning patterns during training.