Sam Altman, the CEO of artificial intelligence lab OpenAI, told a Senate panel he welcomes federal regulation on the technology'to mitigate' its risks. A class-action complaint filed Wednesday in the northern district of California alleges tech leaders OpenAI and Microsoft Corp. used "stolen and misappropriated" information from hundreds of millions of internet users without their knowledge to train and develop its artificial intelligence tech like chatbot ChatGPT. The 16 plaintiffs, who are represented by the Clarkson Law Firm and listed with initials, claimed the defendants "continue to unlawfully collect and feed additional personal data from millions" worldwide to that end and that they systematically scraped 300 billion words from the internet without consent. "Once trained on stolen data, defendants saw the immediate profit potential and rushed the products to market without implementing proper safeguards or controls to ensure that they would not produce or support harmful or malicious content and conduct that could further violate the law, infringe rights and endanger lives," Clarkson continued. "Without these safeguards, the products have already demonstrated their ability to harm humans, in real ways."

A first-of-its-kind law in New York City aims to make the use of AI in hiring and promotion both clearer and fairer. New York's Local Law 144, which goes into effect on 5 July, requires employers to get an independent audit of their automated employment decision tools to ensure that they do not demonstrate significant bias based on sex, race or ethnicity – though it does not cover discrimination based on factors such as …

Canopy CMO Yaron Litwin discusses how criminals are using deepfake technology to blackmail teens and generate child pornography. The rapid advancement of artificial intelligence (AI) programs capable of generating realistic images has led to an explosion in child pornography and blackmail attempts by criminals determined to exploit kids and teenagers. Yaron Litwin, the CMO and Digital Safety Expert for Canopy, a leading AI solution to combat harmful digital content, told Fox News Digital that pedophiles are leveraging the evolving tools in a variety of ways, often with the intent to produce and distribute images of child sexual exploitation across the internet. One of these techniques involves editing a genuine photograph of a fully dressed teenager and turning it into a nude image. In one real-world example offered by Litwin, a 15-year-old boy interested in personal fitness joined an online network of gym enthusiasts. One day, he shared a photo including his bare chest following a workout to the group.

This paper is a contribution to the research on dynamics in assumption-based argumentation (ABA). We investigate situations where a given knowledge base undergoes certain changes. We show that two frequently investigated problems, namely enforcement of a given target atom and deciding strong equivalence of two given ABA frameworks, are intractable in general. Notably, these problems are both tractable for abstract argumentation frameworks (AFs) which admit a close correspondence to ABA by constructing semanticspreserving instances. Inspired by this observation, we search for tractable fragments for ABA frameworks by means of the instantiated AFs. We argue that the usual instantiation procedure is not suitable for the investigation of dynamic scenarios since too much information is lost when constructing the abstract framework. We thus consider an extension of AFs, called cvAFs, equipping arguments with conclusions and vulnerabilities in order to better anticipate their role after the underlying knowledge base is extended. We investigate enforcement and strong equivalence for cvAFs and present syntactic conditions to decide them. We show that the correspondence between cvAFs and ABA frameworks is close enough to capture dynamics in ABA. This yields the desired tractable fragment. We furthermore discuss consequences for the corresponding problems for logic programs.

This paper presents an investigation into machine learning techniques for violence detection in videos and their adaptation to a federated learning context. The study includes experiments with spatio-temporal features extracted from benchmark video datasets, comparison of different methods, and proposal of a modified version of the "Flow-Gated" architecture called "Diff-Gated." Additionally, various machine learning techniques, including super-convergence and transfer learning, are explored, and a method for adapting centralized datasets to a federated learning context is developed. The research achieves better accuracy results compared to state-of-the-art models by training the best violence detection model in a federated learning context.

The use of social robots as instruments for social mediation has been gaining traction in the field of Human-Robot Interaction (HRI). So far, the design of such robots and their behaviors is often driven by technological platforms and experimental setups in controlled laboratory environments. To address complex social relationships in the real world, it is crucial to consider the actual needs and consequences of the situations found therein. This includes understanding when a mediator is necessary, what specific role such a robot could play, and how it moderates human social dynamics. In this paper, we discuss six relevant roles for robotic mediators that we identified by investigating a collection of videos showing realistic group situations. We further discuss mediation behaviors and target measures to evaluate the success of such interventions. We hope that our findings can inspire future research on robot-assisted social mediation by highlighting a wider set of mediation applications than those found in prior studies. Specifically, we aim to inform the categorization and selection of interaction scenarios that reflect real situations, where a mediation robot can have a positive and meaningful impact on group dynamics.

Social mediator robots facilitate human-human interactions by producing behavior strategies that positively influence how humans interact with each other in social settings. As robots for social mediation gain traction in the field of human-human-robot interaction, their ability to "understand" the humans in their environments becomes crucial. This objective requires models of human understanding that consider multiple humans in an interaction as a collective entity and represent the group dynamics that exist among its members. Group dynamics are defined as the influential actions, processes, and changes that occur within and between group interactants. Since an individual's behavior may be deeply influenced by their interactions with other group members, the social dynamics existing within a group can influence the behaviors, attitudes, and opinions of each individual and the group as a whole. Therefore, models of group dynamics are critical for a social mediator robot to be effective in its role. In this paper, we survey existing models of group dynamics and categorize them into models of social dominance, affect, social cohesion, conflict resolution, and engagement. We highlight the multimodal features these models utilize, and emphasize the importance of capturing the interpersonal aspects of a social interaction. Finally, we make a case for models of relational affect as an approach that may be able to capture a representation of human-human interactions that can be useful for social mediation.

Legal language can be understood as the language typically used by those engaged in the legal profession and, as such, it may come both in spoken or written form. Recent legislation on cybersecurity obviously uses legal language in writing, thus inheriting all its interpretative complications due to the typical abundance of cases and sub-cases as well as to the general richness in detail. This paper faces the challenge of the essential interpretation of the legal language of cybersecurity, namely of the extraction of the essential Parts of Speech (POS) from the legal documents concerning cybersecurity. The challenge is overcome by our methodology for POS tagging of legal language. It leverages state-of-the-art open-source tools for Natural Language Processing (NLP) as well as manual analysis to validate the outcomes of the tools. As a result, the methodology is automated and, arguably, general for any legal language following minor tailoring of the preprocessing step. It is demonstrated over the most relevant EU legislation on cybersecurity, namely on the NIS 2 directive, producing the first, albeit essential, structured interpretation of such a relevant document. Moreover, our findings indicate that tools such as SpaCy and ClausIE reach their limits over the legal language of the NIS 2.

Dynamic learning systems subject to selective labeling exhibit censoring, i.e. persistent negative predictions assigned to one or more subgroups of points. In applications like consumer finance, this results in groups of applicants that are persistently denied and thus never enter into the training data. In this work, we formalize censoring, demonstrate how it can arise, and highlight difficulties in detection. We consider safeguards against censoring - recourse and randomized-exploration - both of which ensure we collect labels for points that would otherwise go unobserved. The resulting techniques allow examples from censored groups to enter into the training data and correct the model. Our results highlight the otherwise unmeasured harms of censoring and demonstrate the effectiveness of mitigation strategies across a range of data generating processes.

When Elon Musk arrived at VivaTech, a leading technology conference in France, his presence had an immediate effect, as event founder Maurice Levy of Publicis Groupe was quick to point out. Suddenly everyone wanted to be there. Musk's visit represented a substantial investment for the organization, with rumors of a fee of around a million euros, private plane excluded. The tech star's dazzle may have dimmed somewhat, but innovators still welcome him with open arms. An admitted introvert who can also be eloquent and at times even poetic, Musk answered every question he was asked.