Domestic violence, which is often perceived as a gendered issue among female victims, has gained increasing attention in recent years. Despite this focus, male victims of domestic abuse remain primarily overlooked, particularly in Bangladesh. Our study represents a pioneering exploration of the underexplored realm of male domestic violence (MDV) within the Bangladeshi context, shedding light on its prevalence, patterns, and underlying factors. Existing literature predominantly emphasizes female victimization in domestic violence scenarios, leading to an absence of research on male victims. We collected data from the major cities of Bangladesh and conducted exploratory data analysis to understand the underlying dynamics. We implemented 11 traditional machine learning models with default and optimized hyperparameters, 2 deep learning, and 4 ensemble models. Despite various approaches, CatBoost has emerged as the top performer due to its native support for categorical features, efficient handling of missing values, and robust regularization techniques, achieving 76% accuracy. In contrast, other models achieved accuracy rates in the range of 58-75%. The eXplainable AI techniques, SHAP and LIME, were employed to gain insights into the decision-making of black-box machine learning models. By shedding light on this topic and identifying factors associated with domestic abuse, the study contributes to identifying groups of people vulnerable to MDV, raising awareness, and informing policies and interventions aimed at reducing MDV. Our findings challenge the prevailing notion that domestic abuse primarily affects women, thus emphasizing the need for tailored interventions and support systems for male victims. ML techniques enhance the analysis and understanding of the data, providing valuable insights for developing effective strategies to combat this pressing social issue.

Electrical engineer Gilbert Herrera was appointed research director of the US National Security Agency in late 2021, just as an AI revolution was brewing inside the US tech industry. The NSA, sometimes jokingly said to stand for No Such Agency, has long hired top math and computer science talent. Its technical leaders have been early and avid users of advanced computing and AI. And yet when Herrera spoke with me by phone about the implications of the latest AI boom from NSA headquarters in Fort Meade, Maryland, it seemed that, like many others, the agency has been stunned by the recent success of the large language models behind ChatGPT and other hit AI products. The conversation has been lightly edited for clarity and length.

An interactive exhibit opening Wednesday at the National WWII Museum will use artificial intelligence to let visitors hold virtual conversations with images of veterans, including a Medal of Honor winner who died in 2022. Voices From the Front will also enable visitors to the New Orleans museum to ask questions of war-era home front heroes and supporters of the U.S. war effort -- including a military nurse who served in the Philippines, an aircraft factory worker, and Margaret Kerry, a dancer who performed at USO shows and, after the war, was a model for the Tinker Bell character in Disney productions. Four years in the making, the project incorporates video-recorded interviews with 18 veterans of the war or the support effort -- each of them having sat for as many as a thousand questions about the war and their personal lives. Among the participants was Marine Corps veteran Hershel Woodrow "Woody" Wilson, a Medal of Honor Winner who fought at Iwo Jima, Japan. He died in June 2022 after recording his responses.

The AAAI/SIGAI Doctoral Consortium provides an opportunity for a group of PhD students to discuss and explore their research interests and career objectives in an interdisciplinary workshop together with a panel of established researchers. This year, 30 students were selected for this programme, and we've been meeting them and talking about their research. In this interview, Raffaele Galliera, tells us about his work on deep reinforcement learning for communication networks. My name is Raffaele Galliera and I'm a PhD student in the Intelligent Systems and Robotics program at the University of West Florida, located in Pensacola. It's a joint program between the University of West Florida and the Institute for Human and Machine Cognition (IHMC), which is a nonprofit organization based in Pensacola.

AI algorithms used in the public sector, e.g., for allocating social benefits or predicting fraud, often involve multiple public and private stakeholders at various phases of the algorithm's life-cycle. Communication issues between these diverse stakeholders can lead to misinterpretation and misuse of algorithms. We investigate the communication processes for AI fairness-related decisions by conducting interviews with practitioners working on algorithmic systems in the public sector. By applying qualitative coding analysis, we identify key elements of communication processes that underlie fairness-related human decisions. We analyze the division of roles, tasks, skills, and challenges perceived by stakeholders. We formalize the underlying communication issues within a conceptual framework that i. represents the communication patterns ii. outlines missing elements, such as actors who miss skills for their tasks. The framework is used for describing and analyzing key organizational issues for fairness-related decisions. Three general patterns emerge from the analysis: 1. Policy-makers, civil servants, and domain experts are less involved compared to developers throughout a system's life-cycle. This leads to developers taking on extra roles such as advisor, while they potentially miss the required skills and guidance from domain experts. 2. End-users and policy-makers often lack the technical skills to interpret a system's limitations, and rely on developer roles for making decisions concerning fairness issues. 3. Citizens are structurally absent throughout a system's life-cycle, which may lead to decisions that do not include relevant considerations from impacted stakeholders.

The rise in the use of AI/ML applications across industries has sparked more discussions about the fairness of AI/ML in recent times. While prior research on the fairness of AI/ML exists, there is a lack of empirical studies focused on understanding the views and experiences of AI practitioners in developing a fair AI/ML. Understanding AI practitioners' views and experiences on the fairness of AI/ML is important because they are directly involved in its development and deployment and their insights can offer valuable real-world perspectives on the challenges associated with ensuring fairness in AI/ML. We conducted semi-structured interviews with 22 AI practitioners to investigate their understanding of what a 'fair AI/ML' is, the challenges they face in developing a fair AI/ML, the consequences of developing an unfair AI/ML, and the strategies they employ to ensure AI/ML fairness. We developed a framework showcasing the relationship between AI practitioners' understanding of 'fair AI/ML' and (i) their challenges in its development, (ii) the consequences of developing an unfair AI/ML, and (iii) strategies used to ensure AI/ML fairness. Additionally, we also identify areas for further investigation and offer recommendations to aid AI practitioners and AI companies in navigating fairness.

We develop Polaris, the first safety-focused LLM constellation for real-time patient-AI healthcare conversations. Unlike prior LLM works in healthcare focusing on tasks like question answering, our work specifically focuses on long multi-turn voice conversations. Our one-trillion parameter constellation system is composed of several multibillion parameter LLMs as co-operative agents: a stateful primary agent that focuses on driving an engaging conversation and several specialist support agents focused on healthcare tasks performed by nurses to increase safety and reduce hallucinations. We develop a sophisticated training protocol for iterative co-training of the agents that optimize for diverse objectives. We train our models on proprietary data, clinical care plans, healthcare regulatory documents, medical manuals, and other medical reasoning documents. We align our models to speak like medical professionals, using organic healthcare conversations and simulated ones between patient actors and experienced nurses. This allows our system to express unique capabilities such as rapport building, trust building, empathy and bedside manner. Finally, we present the first comprehensive clinician evaluation of an LLM system for healthcare. We recruited over 1100 U.S. licensed nurses and over 130 U.S. licensed physicians to perform end-to-end conversational evaluations of our system by posing as patients and rating the system on several measures. We demonstrate Polaris performs on par with human nurses on aggregate across dimensions such as medical safety, clinical readiness, conversational quality, and bedside manner. Additionally, we conduct a challenging task-based evaluation of the individual specialist support agents, where we demonstrate our LLM agents significantly outperform a much larger general-purpose LLM (GPT-4) as well as from its own medium-size class (LLaMA-2 70B).

It's been just over a week since Kate Middleton, the internet's favorite "missing person," claimed that a photoshopped image of her with her children on U.K. Mother's Day was edited by her, for unspecified reasons. Then, on Monday, we had our first recorded sighting of the princess, out shopping with Prince William at the Royal Farms Windsor Farm Shop, near Windsor Castle. The video was released by TMZ and the Sun, and stills from it were plastered on the front pages of all the British tabloids Tuesday. Supposedly, it was taken by a 40-year-man, Nelson Silva, who lives nearby and was quoted in TMZ as saying: "Kate looked happy and relaxed. They look happy just to be able to go to a shop and mingle.

The discussion on causality in human history dates back to ancient Greece, yet to this day, there is still no consensus. Fundamentally, this stems from the nature of human cognition, as understanding causality requires abstract tools to transcend the limitations of human cognition. In recent decades, the rapid development of mathematical and computational tools has provided new theoretical and technical means for exploring causality, creating more avenues for investigation. Based on this, this paper introduces a new definition of causality using category theory, proposed by Samuel Eilenberg and Saunders Mac Lane in 1945 to avoid the self-referential contradictions in set theory, notably the Russell paradox. Within this framework, the feasibility of indicator synthesis in causal inference is demonstrated. Due to the limitations in the development of category theory-related technical tools, this paper adopts the widely-used probabilistic causal graph tool proposed by Judea Pearl in 1995 to study the application of causal inference in personal credit risk management. The specific work includes: research on the construction method of causal inference index system, definition of causality and feasibility proof of indicator synthesis causal inference within this framework, application methods of causal graph model and intervention alternative criteria in personal credit risk management, and so on.

We present a knowledge integration framework (called KIF) that uses Wikidata as a lingua franca to integrate heterogeneous knowledge bases. These can be triplestores, relational databases, CSV files, etc., which may or may not use the Wikidata dialect of RDF. KIF leverages Wikidata's data model and vocabulary plus user-defined mappings to expose a unified view of the integrated bases while keeping track of the context and provenance of their statements. The result is a virtual knowledge base which behaves like an "extended Wikidata" and which can be queried either through an efficient filter interface or using SPARQL. We present the design and implementation of KIF, discuss how we have used it to solve a real integration problem in the domain of chemistry (involving Wikidata, PubChem, and IBM CIRCA), and present experimental results on the performance and overhead of KIF.