In everyday life, people make countless reasonableness judgments that determine appropriate behavior in various contexts. Predicting these judgments challenges the legal system, as judges' intuitions may not align with broader societal views. This Article investigates whether large language models (LLMs) can learn to identify patterns driving human reasonableness judgments. Using randomized controlled trials comparing humans and models across multiple legal contexts with over 10,000 simulated judgments, we demonstrate that certain models capture not just surface-level responses but potentially their underlying decisional architecture. Strikingly, these systems prioritize social cues over economic efficiency in negligence determinations, mirroring human behavior despite contradicting textbook treatments. These findings suggest practical applications: judges could calibrate intuitions against broader patterns, lawmakers could test policy interpretations, and resource-constrained litigants could preview argument reception. As AI agents increasingly make autonomous real-world decisions, understanding whether they've internalized recognizable ethical frameworks becomes essential for anticipating their behavior.

The accelerated evolution of digital infrastructures and algorithmic systems is reshaping how the humanities engage with knowledge and culture. Rooted in the traditions of Digital Humanities and Digital Humanism, the concept of "Cyber Humanities" proposes a critical reconfiguration of humanistic inquiry for the post-digital era. This Manifesto introduces a flexible framework that integrates ethical design, sustainable digital practices, and participatory knowledge systems grounded in human-centered approaches. By means of a Decalogue of foundational principles, the Manifesto invites the scientific community to critically examine and reimagine the algorithmic infrastructures that influence culture, creativity, and collective memory. Rather than being a simple extension of existing practices, "Cyber Humanities" should be understood as a foundational paradigm for humanistic inquiry in a computationally mediated world. Keywords: Cyber Humanities, Digital Humanities, Transdisciplinary Epistemology, Algorithmic Reflexivity, Human-centered AI, Ethics-by-Design, Knowledge Ecosystems, Digital Sovereignty, Cognitive Infrastructures

Large-scale datasets are foundational for research and development in natural language processing. However, current approaches face three key challenges: (1) reliance on ambiguously licensed sources restricting use, sharing, and derivative works; (2) static dataset releases that prevent community contributions and diminish longevity; and (3) quality assurance processes restricted to publishing teams rather than leveraging community expertise. To address these limitations, we introduce two contributions: the Dynaword approach and Danish Dynaword. The Dynaword approach is a framework for creating large-scale, open datasets that can be continuously updated through community collaboration. Danish Dynaword is a concrete implementation that validates this approach and demonstrates its potential. Danish Dynaword contains over four times as many tokens as comparable releases, is exclusively openly licensed, and has received multiple contributions across industry and research. The repository includes light-weight tests to ensure data formatting, quality, and documentation, establishing a sustainable framework for ongoing community contributions and dataset evolution.

Two Los Angeles County residents face federal charges after they were arrested on suspicion of illegally exporting tens of millions of dollars' worth of artificial intelligence microchips to China, authorities said. Chuan Geng, 28, of Pasadena; and Shiwei Yang, 28, of El Monte, were taken into custody on Saturday for their alleged involvement in the illegal overseas export of processing units used in modern computing and artificial intelligence applications, according to a statement from the U.S. attorney's office for the Eastern District of California. Federal prosecutors said both were Chinese nationals, though Geng is a lawful permanent resident of the U.S. Yang, however, was in the country illegally as she had overstayed her visa, according to authorities. Yaoning'Mike' Sun of Chino Hills is charged with acting as an illegal agent of a foreign power and conspiring to advance China-friendly policies in local government. In a criminal complaint, U.S. Justice Department officials alleged the pair had "knowingly and willingly" undercut federal export regulations to conceal illegal shipments to China for nearly three years.

California gubernatorial candidate Steve Hilton on former Vice President Kamala Harris declining to run for the California governorship and Gov. Gavin Newsom's idea to redraw California's map if Texas redistricts. California Democrats are once again trying to rig the system, overturn elections and steal congressional seats from Republicans. Gov. Gavin Newsom and Attorney General Rob Bonta are planning to redraw California's congressional maps in 2025 or 2026, halfway through the decade and years before the next census. It's a blatant, unconstitutional power grab designed to silence millions of voters and cement one-party rule in California. Democrats are already trying to rewrite the history of this redistricting fight, claiming it's just retaliation for Republican maps in Texas.

This situation can give rise to privacy concerns, as organizations may not want to share sensitive information; for instance, healthcare providers may be reluctant to share patient information and security system maintainers may not want to risk sharing facial recognition data for system performance updates. Additionally, there may be issues with obtaining the source data such as when it is hard to retrieve due to technical difficulties or intellectual property restrictions (Li et al., 2020b; Chen et al., 2021; Liang et al., 2020; Ahmed et al., 2021b). Recent advancements in source-free unsupervised domain adaptation (SFUDA) have presented solutions for a scenario where source data is not accessible (Fang et al., 2022). Purposely, SFUDA utilizes pre-trained source models to improve the generalization of a model on an unlabeled target dataset. Our work is similar to other approaches in the field of SFUDA (Li et al., 2020b; Chen et al., 2021; Liang et al., 2020; Ahmed et al., 2021b), in that it addresses the practical scenario where source data is not available during training. Importantly, a crucial aspect is often overlooked by the majority of SFUDA studies. When it is assumed that source data is not accessible, then it cannot be guaranteed that the available source models have been trained on domains related to the target task. And yet, most of the works only have experimented on classic domain adaptation benchmarks, which are somewhat related by design, e.g., Digits-Five (Peng et al., 2019), Office-31 (Saenko et al., 2010), and Office-Home (Venkateswara et al., 2017), i.e,, domains that share the same labels but are dissimilar in feature (and ambient) space. Our approach is unique in that we consider such a source-free supervised transfer learning (SFSTL) setting (Lee et al., 2019), where we do not assume source models are trained on tasks with similar feature spaces or 1

Counterfactual explanations study what should have changed in order to get an alternative result, enabling end-users to understand machine learning mechanisms with counterexamples. Actionability is defined as the ability to transform the original case to be explained into a counterfactual one. We develop a method for actionable counterfactual explanations that, unlike predecessors, does not directly leverage training data. Rather, data is only used to learn a density estimator, creating a search landscape in which to apply path planning algorithms to solve the problem and masking the endogenous data, which can be sensitive or private. We put special focus on estimating the data density using Bayesian networks, demonstrating how their enhanced interpretability is useful in high-stakes scenarios in which fairness is raising concern. Using a synthetic benchmark comprised of 15 datasets, our proposal finds more actionable and simpler counterfactuals than the current state-of-the-art algorithms. We also test our algorithm with a real-world Environmental Protection Agency dataset, facilitating a more efficient and equitable study of policies to improve the quality of life in United States of America counties. Our proposal captures the interaction of variables, ensuring equity in decisions, as policies to improve certain domains of study (air, water quality, etc.) can be detrimental in others. In particular, the sociodemographic domain is often involved, where we find important variables related to the ongoing housing crisis that can potentially have a severe negative impact on communities.

Computer perception (CP) technologies (digital phenotyping, affective computing and related passive sensing approaches) offer unprecedented opportunities to personalize healthcare, but provoke concerns about privacy, bias and the erosion of empathic, relationship-centered practice. A comprehensive understanding of perceived risks, benefits, and implementation challenges from those who design, deploy and experience these tools in real-world settings remains elusive. This study provides the first evidence-based account of key stakeholder perspectives on the relational, technical, and governance challenges raised by the integration of CP technologies into patient care. We conducted in-depth, semi-structured interviews with 102 stakeholders: adolescent patients and their caregivers, frontline clinicians, technology developers, and ethics, legal, policy or philosophy scholars. Transcripts underwent thematic analysis by a multidisciplinary team; reliability was enhanced through double coding and consensus adjudication. Stakeholders articulated seven interlocking concern domains: (1) trustworthiness and data integrity; (2) patient-specific relevance; (3) utility and workflow integration; (4) regulation and governance; (5) privacy and data protection; (6) direct and indirect patient harms; and (7) philosophical critiques of reductionism. To operationalize humanistic safeguards, we propose "personalized roadmaps": co-designed plans that predetermine which metrics will be monitored, how and when feedback is shared, thresholds for clinical action, and procedures for reconciling discrepancies between algorithmic inferences and lived experience. By translating these insights into personalized roadmaps, we offer a practical framework for developers, clinicians and policymakers seeking to harness continuous behavioral data while preserving the humanistic core of care.

Recent advances in denoising diffusion models have enabled rapid generation of optimized structures for topology optimization. However, these models often rely on surrogate predictors to enforce physical constraints, which may fail to capture subtle yet critical design flaws such as floating components or boundary discontinuities that are obvious to human experts. In this work, we propose a novel human-in-the-loop diffusion framework that steers the generative process using a lightweight reward model trained on minimal human feedback. Inspired by preference alignment techniques in generative modeling, our method learns to suppress unrealistic outputs by modulating the reverse diffusion trajectory using gradients of human-aligned rewards. Specifically, we collect binary human evaluations of generated topologies and train classifiers to detect floating material and boundary violations. These reward models are then integrated into the sampling loop of a pre-trained diffusion generator, guiding it to produce designs that are not only structurally performant but also physically plausible and manufacturable. Our approach is modular and requires no retraining of the diffusion model. Preliminary results show substantial reductions in failure modes and improved design realism across diverse test conditions. This work bridges the gap between automated design generation and expert judgment, offering a scalable solution to trustworthy generative design.

Vision Transformers (ViTs) have revolutionized computer vision tasks with their exceptional performance. However, the introduction of privacy regulations such as GDPR and CCP A has brought new challenges to them. These laws grant users the right to withdraw their data, necessitating not only the deletion of data but also the complete removal of its influence from trained models. Machine unlearning emerges as a critical solution, with exact unlearning being computationally prohibitive and approximate methods offering a more practical approach. This work addresses the particularly challenging scenario of random data forgetting in ViTs, where the model must forget specific samples while retaining others, even within the same class. We first reveal the core characteristics of ViTs through selective masking experiments: when high-attention areas are masked, the model retains its recognition capability but significantly weakens its memorization ability. Based on the above insights, we propose LetheViT, a contrastive unlearning method tailored for ViTs. LetheViT uses masked image inputs to generate positive logits and original image inputs to generate negative logits, guiding the model to forget specific details while retaining the general cl category outlines. Experimental results demonstrate that LetheViT achieves state-of-the-art performance, effectively balancing privacy compliance with model efficacy.