March 2026

arXiv:2603.10021v1 Announce Type: new Abstract: Gemini said Lawmakers today face continuous calls to “future proof” the legal system against generative artificial intelligence, algorithmic decision-making, targeted advertising, and all manner of emerging technologies. This Article takes a contrarian stance: it is not the law that needs bolstering for the future, but the future that needs protection from the law. From the printing press and the elevator to ChatGPT and online deepfakes, the recurring historical pattern is familiar. Technological breakthroughs […]

Ver mais

arXiv:2603.10020v1 Announce Type: new Abstract: Dark patterns in online commerce, especially deceptive user interface designs for apps and websites, undermine consumer autonomy and distort online markets. Although sometimes deception is intentional, the complex app development process can also unintentionally produce manipulative user interfaces. This paper discusses common design pitfalls and proposes strategies for app makers to avoid infringing user autonomy or incurring legal liability under emerging principles of consumer protection law. By focusing on choice architecture and transparent […]

Ver mais

arXiv:2603.10019v1 Announce Type: new Abstract: Increasingly artificial intelligence (AI) has been cast in “god-like” roles (to name a few: film industry – Matrix, The Creator, Mission Impossible, Foundation, Dune etc.; literature – Children of Time, Permutation City, Neuromancer, I Have no Mouth and I Must Scream, Alphaville etc.). This trend has accelerated with the advent of sophisticated Large Language Models such as ChatGPT. For this phenomenon, where AI is perceived as divine, we use the term GPTheology, where […]

Ver mais

arXiv:2603.10018v1 Announce Type: new Abstract: As large language models (LLMs) become pervasive as assistants and thought partners, it is important to characterize their persuasive influence on users’ beliefs. However, a central challenge is to distinguish “beneficial” from “harmful” forms of influence, in a manner that is normatively defensible and legitimate. We propose DeliberationBench, a benchmark for assessing LLM influence that takes the process of deliberative opinion polling as its standard. We demonstrate our approach in a preregistered randomized […]

Ver mais

arXiv:2603.10017v1 Announce Type: new Abstract: A global challenge in artificial intelligence (AI) regulation lies in achieving effective risk management without compromising innovation and technical progress. The European Union (EU) Artificial Intelligence Act represents the first regulatory attempt worldwide to navigate this tension in the form of a binding, risk-based framework. In August 2025, obligations for providers of general-purpose AI (GPAI) models under the EU AI Act entered into application. They require providers of the most advanced GPAI models […]

Ver mais

arXiv:2603.10016v1 Announce Type: new Abstract: We investigate whether large language models (LLMs) display human-like cognitive biases, focusing on potential implications for assistance in judicial sentencing, a decision-making system where fairness is paramount. Two of the most relevant biases were chosen: the virtuous victim effect (VVE), with emphasis given to its reduction when adjacent consent is present, and prestige-based halo effects (occupation, company, and credentials). Using vignettes that were altered from prior literature to avoid LLMs recalling from their […]

Ver mais

arXiv:2603.10015v1 Announce Type: new Abstract: Frontier AI Safety Policies concentrate on prevention — capability evaluations, deployment gates, and usage constraints — while neglecting institutional capacity to coordinate responses when prevention fails. We argue that this coordination gap is structural: investments in ecosystem robustness yield diffuse benefits but concentrated costs, generating systematic underinvestment. Drawing on risk regimes in nuclear safety, pandemic preparedness, and critical infrastructure, we propose that similar mechanisms — precommitment, shared protocols, and standing coordination venues — […]

Ver mais

arXiv:2603.10014v1 Announce Type: new Abstract: Learning at scale often requires domain-specific automation such as assessment and feedback. An organization locked in to a general learning platform without these specialist automations limits its pedagogical offering. An ecosystem of interoperable, platform-agnostic microservices for domain-specific automation would solve this problem. To develop an effective eco-system, a standard interface (API) for education microservices is required. We propose an initial specification for a standard, platform-independent API for educational microservices, $upmu$Ed. The API integrates […]

Ver mais

arXiv:2603.10013v1 Announce Type: new Abstract: Open Educational Resources (OER) are freely available teaching and learning materials, such as textbooks, videos, and interactive games, that can be used, reused, adapted, and shared. OER can leverage access, collaboration, and innovation in education; however, their adoption and long-term use remain limited. Motivated by this issue, this manuscript examined the literature and identified 26 social, economic, and technical barriers that hinder teachers, students, and institutions from creating, using, and maintaining OER. These […]

Ver mais

arXiv:2603.10012v1 Announce Type: new Abstract: Military Large Language Models (LLMs) must provide accurate information to the warfighter in time-critical and dangerous situations. However, today’s LLMs are imbued with safety behaviors that cause the LLM to refuse many legitimate queries in the military domain, particularly those related to violence, terrorism, or military technology. Our gold benchmark for assessing refusal rates, which was developed by veterans of the US Army and special forces, is to our knowledge the first dataset […]

Ver mais