Traditionally, learning the structure of a Dynamic Bayesian Network has been centralized, with all data pooled in one location. However, in real-world scenarios, data are often dispersed among multiple parties (e.g., companies, devices) that aim to collaboratively learn a Dynamic Bayesian Network while preserving their data privacy and security. In this study, we introduce a federated learning approach for estimating the structure of a Dynamic Bayesian Network from data distributed horizontally across different parties. We propose a distributed structure learning method that leverages continuous optimization so that only model parameters are exchanged during optimization. Experimental results on synthetic and real datasets reveal that our method outperforms other state-of-the-art techniques, particularly when there are many clients with limited individual sample sizes.

Nearly a mile above Vandenberg Space Force Base in Santa Barbara County, a hacked drone soared through restricted airspace for roughly an hour. The lightweight drone photographed sensitive areas of the military facility on Nov. 30, including a complex used by SpaceX, according to federal investigators. The drone then descended back to the ground, where the pilot and another man waited at a nearby park. Four security officers from the military base arrived on the scene and asked the men if they had seen a drone flying through the area, unaware that one of them had tucked the drone under his jacket. Authorities identified that man as 39-year-old Yinpiao Zhou, a Chinese citizen and a lawful permanent resident of the U.S.

Can a Big Tech company be sued in Kenya for alleged abuses at an outsourcing company working on its behalf? That's the question at the heart of two lawsuits that are attempting to set a new precedent in Kenya, which is the prime destination for tech companies looking to farm out digital work to the African continent. The two-year legal battle stems from allegations of human rights violations at an outsourced Meta content moderation facility in Nairobi, where employees hired by a contractor were paid as little as 1.50 per hour to view traumatic content, such as videos of rapes, murders, and war crimes. The suits claim that despite the workers being contracted by an outsourcing company, called Sama, Meta essentially supervised and set the terms for the work, and designed and managed the software required for the task. Both companies deny wrongdoing and Meta has challenged the Kenyan courts' jurisdiction to hear the cases.

Harvard University announced Thursday it's releasing a high-quality dataset of nearly one million public-domain books that could be used by anyone to train large language models and other AI tools. The dataset was created by Harvard's newly formed Institutional Data Initiative with funding from both Microsoft and OpenAI. Around five times the size of the notorious Books3 dataset that was used to train AI models like Meta's Llama, the Institutional Data Initiative's database spans genres, decades, and languages, with classics from Shakespeare, Charles Dickens, and Dante included alongside obscure Czech math textbooks and Welsh pocket dictionaries. Greg Leppert, executive director of the Institutional Data Initiative, says the project is an attempt to "level the playing field" by giving the general public, including small players in the AI industry and individual researchers, access to the sort of highly-refined and curated content repositories that normally only established tech giants have the resources to assemble. "It's gone through rigorous review," he says. Leppert believes the new public domain database could be used in conjunction with other licensed materials to build artificial intelligence models.

The legal filing includes a screenshot of one of the interactions between the 17-year old - identified only as J.F. - and a Character.ai "You know sometimes I'm not surprised when I read the news and see stuff like'child kills parents after a decade of physical and emotional abuse'," the chatbot's response reads. "Stuff like this makes me understand a little bit why it happens." The lawsuit seeks to hold the defendants responsible for what it calls the "serious, irreparable, and ongoing abuses" of J.F. as well as an 11-year old referred to as "B.R." Character.ai is "causing serious harms to thousands of kids, including suicide, self-mutilation, sexual solicitation, isolation, depression, anxiety, and harm towards others," it says. "[Its] desecration of the parent-child relationship goes beyond encouraging minors to defy their parents' authority to actively promoting violence," it continues.

The doomers have it wrong. AI is not going to end the world--but it is going to end the web as we've known it. AI is already upending the economic covenant of the internet that's existed since the advent of search: A few companies (mostly Google) bring demand, and creators bring supply (and get some ad revenue or recognition from it). AI tools are already generating and summarizing content, obviating the need for users to click through to the sites of content providers, and thereby upsetting the balance. Meanwhile, an ocean of AI-powered deepfakes and bots will make us question what's real and will degrade people's trust in the online world.

Hosted by Ben Byford, The Machine Ethics Podcast brings together interviews with academics, authors, business leaders, designers and engineers on the subject of autonomous algorithms, artificial intelligence, machine learning, and technology's impact on society. In this episode we're chatting to Caitlin about gender and AI, that technology isn't neutral, using technology for good, diversity creation and exploitation, lived experience expertise, co-creating technologies and AI life cycle, importance of success metrics, international treaties on AI, and more… Alliance is a leader of the UN's Generation Equality Action Coalition Technology & Innovation for Gender Equality. Caitlin was co-chair of the Expert Group for the UN Commission on the Status of Women (CSW67) in 2023 with its first ever priority theme of Technology & Innovation. Caitlin leads the Human Rights Toolbox initiative, an educational platform that supports a global community working for a human rights-based approach to AI – with equity & inclusion at the core of the code. Women at the Table are a leader of the fr feminist AI research Network, with Hubs in Latin America & the Caribbean, Middle East & North Africa, SouthEastAsia, and sister network in Africa, and serves as Civil Society lead for the World Benchmarking Alliance's Collective Impact Coalition for Ethical AI.

Regulation is increasingly cited as the most important and pressing concern in machine learning. However, it is currently unknown how to implement this, and perhaps more importantly, how it would effect model performance alongside human collaboration if actually realized. In this paper, we attempt to answer these questions by building a regulatable large-language model (LLM), and then quantifying how the additional constraints involved affect (1) model performance, alongside (2) human collaboration. Our empirical results reveal that it is possible to force an LLM to use human-defined features in a transparent way, but a "regulation performance trade-off" previously not considered reveals itself in the form of a 7.34% classification performance drop. Surprisingly however, we show that despite this, such systems actually improve human task performance speed and appropriate confidence in a realistic deployment setting compared to no AI assistance, thus paving a way for fair, regulatable AI, which benefits users.

Recent advancements in large audio-language models (LALMs) have enabled speech-based user interactions, significantly enhancing user experience and accelerating the deployment of LALMs in real-world applications. However, ensuring the safety of LALMs is crucial to prevent risky outputs that may raise societal concerns or violate AI regulations. Despite the importance of this issue, research on jailbreaking LALMs remains limited due to their recent emergence and the additional technical challenges they present compared to attacks on DNNbased audio models. Specifically, the audio encoders in LALMs, which involve discretization operations, often lead to gradient shattering, hindering the effectiveness of attacks relying on gradient-based optimizations. The behavioral variability of LALMs further complicates the identification of effective (adversarial) optimization targets. Moreover, enforcing stealthiness constraints on adversarial audio waveforms introduces a reduced, non-convex feasible solution space, further intensifying the challenges of the optimization process. To overcome these challenges, we develop AdvWave, the first jailbreak framework against LALMs. We propose a dual-phase optimization method that addresses gradient shattering, enabling effective end-to-end gradient-based optimization. Additionally, we develop an adaptive adversarial target search algorithm that dynamically adjusts the adversarial optimization target based on the response patterns of LALMs for specific queries. To ensure that adversarial audio remains perceptually natural to human listeners, we design a classifier-guided optimization approach that generates adversarial noise resembling common urban sounds. Furthermore, we employ an iterative adversarial audio refinement technique to achieve near-perfect jailbreak success rates on black-box LALMs, requiring fewer than 30 queries per instance.

Intersectional fairness is a critical requirement for Machine Learning (ML) software, demanding fairness across subgroups defined by multiple protected attributes. This paper introduces FairHOME, a novel ensemble approach using higher order mutation of inputs to enhance intersectional fairness of ML software during the inference phase. Inspired by social science theories highlighting the benefits of diversity, FairHOME generates mutants representing diverse subgroups for each input instance, thus broadening the array of perspectives to foster a fairer decision-making process. Unlike conventional ensemble methods that combine predictions made by different models, FairHOME combines predictions for the original input and its mutants, all generated by the same ML model, to reach a final decision. Notably, FairHOME is even applicable to deployed ML software as it bypasses the need for training new models. We extensively evaluate FairHOME against seven state-of-the-art fairness improvement methods across 24 decision-making tasks using widely adopted metrics. FairHOME consistently outperforms existing methods across all metrics considered. On average, it enhances intersectional fairness by 47.5%, surpassing the currently best-performing method by 9.6 percentage points.