The recent request goes against decades of precedent and puts noncitizens at further risk of immigration enforcement actions. Workers at the Social Security Administration have been told to share information about in-person appointments with agents of Immigration and Customs Enforcement, WIRED has learned. "If ICE comes in and asks if someone has an upcoming appointment, we will let them know the date and time," an employee with direct knowledge of the directive says. They spoke on the condition of anonymity for fear of retaliation. While the majority of appointments with SSA take place over the phone, some appointments still happen in person.

Social Security Administration Commissioner Frank Bisignano provides an update on the agency's work on'The Claman Countdown.' President Donald Trump's pick to head the nation's Social Security apparatus, Commissioner Frank Bisignano, told Fox News Digital that criticisms of the Trump administration's approach to Social Security are politically motivated and misleading. Democrats have expressed a wide range of concerns about Social Security under the current administration, including claims the Trump administration is making it more difficult for seniors and people with disabilities to access their benefits. The Trump administration's critics have also expressed concern that the president is seeking to privatize the program and is exaggerating fraud concerns to justify sweeping reforms. Democrats in Congress have gone as far as launching a "Social Security War Room" to coordinate their efforts to fight back.

Artificial intelligence researchers have proposed various data-driven algorithms to improve the processes that match individuals experiencing homelessness to scarce housing resources. It remains unclear whether and how these algorithms are received or adopted by practitioners and what their corresponding consequences are. Through semi-structured interviews with 13 policymakers in homeless services in Los An-geles, we investigate whether such change-makers are open to the idea of integrating AI into the housing resource matching process, identifying where they see potential gains and drawbacks from such a system in issues of efficiency, fairness, and transparency. Our qualitative analysis indicates that, even when aware of various complicating factors, policymak-ers welcome the idea of an AI matching tool if thoughtfully designed and used in tandem with human decision-makers. Though there is no consensus as to the exact design of such an AI system, insights from policymakers raise open questions and design considerations that can be enlightening for future researchers and practitioners who aim to build responsible algorithmic systems to support decision-making in low-resource scenarios.

Since Elon Musk announced that he'll be stepping back from his daily work with DOGE, perhaps you've been wondering if he has anything else to fill that time now that he's shut down operations at America's humanitarian-aid provider, wrecked much of the nation's scientific-research infrastructure, and disputed the communications systems at the Social Security Administration. One way to find out would be to ask Grok, his entry in the A.I. sweepstakes. "Elon Musk's artificial intelligence company, xAI, has been making significant moves in Memphis," Grok reports. "But these have sparked controversy." The Lede Reporting and commentary on what you need to know today.

Amidst the chaos and upheaval at the Social Security Administration (SSA) caused by Elon Musk's so-called Department of Government Efficiency (DOGE), employees have now been asked to integrate the use of a generative AI chatbot into their daily work. But before any of them can use it, they all need to watch a four-minute training video featuring an animated, four-fingered woman crudely drawn in a style that would not look out of place on websites created in the early part of this century. Aside from the Web 1.0-era graphics employed, the video also fails at its primary purpose of informing SSA staff about one of the most important aspects of using the chatbot: Do not use any personally identifiable information (PII) when using the assistant. There is nothing wrong with your speakers; WIRED has disabled the sound. "Our apologies for the oversight in our training video," the SSA wrote in a fact sheet about the chatbot that was shared in an email to employees last week.

Estimating the causal effects of an intervention on outcomes is crucial. But often in domains such as healthcare and social services, this critical information about outcomes is documented by unstructured text, e.g. clinical notes in healthcare or case notes in social services. For example, street outreach to homeless populations is a common social services intervention, with ambiguous and hard-to-measure outcomes. Outreach workers compile case note records which are informative of outcomes. Although experts can succinctly extract relevant information from such unstructured case notes, it is costly or infeasible to do so for an entire corpus, which can span millions of notes. Recent advances in large language models (LLMs) enable scalable but potentially inaccurate annotation of unstructured text data. We leverage the decision of which datapoints should receive expert annotation vs. noisy imputation under budget constraints in a "design-based" estimator combining limited expert and plentiful noisy imputation data via \textit{causal inference with missing outcomes}. We develop a two-stage adaptive algorithm that optimizes the expert annotation probabilities, estimating the ATE with optimal asymptotic variance. We demonstrate how expert labels and LLM annotations can be combined strategically, efficiently and responsibly in a causal estimator. We run experiments on simulated data and two real-world datasets, including one on street outreach, to show the versatility of our proposed method.

This paper examines the responsible integration of artificial intelligence (AI) in human services organizations (HSOs), proposing a nuanced framework for evaluating AI applications across multiple dimensions of risk. The authors argue that ethical concerns about AI deployment -- including professional judgment displacement, environmental impact, model bias, and data laborer exploitation -- vary significantly based on implementation context and specific use cases. They challenge the binary view of AI adoption, demonstrating how different applications present varying levels of risk that can often be effectively managed through careful implementation strategies. The paper highlights promising solutions, such as local large language models, that can facilitate responsible AI integration while addressing common ethical concerns. The authors propose a dimensional risk assessment approach that considers factors like data sensitivity, professional oversight requirements, and potential impact on client wellbeing. They conclude by outlining a path forward that emphasizes empirical evaluation, starting with lower-risk applications and building evidence-based understanding through careful experimentation. This approach enables organizations to maintain high ethical standards while thoughtfully exploring how AI might enhance their capacity to serve clients and communities effectively.

Word embeddings represent a transformative technology for analyzing text data in social work research, offering sophisticated tools for understanding case notes, policy documents, research literature, and other text-based materials. This methodological paper introduces word embeddings to social work researchers, explaining how these mathematical representations capture meaning and relationships in text data more effectively than traditional keyword-based approaches. We discuss fundamental concepts, technical foundations, and practical applications, including semantic search, clustering, and retrieval augmented generation. The paper demonstrates how embeddings can enhance research workflows through concrete examples from social work practice, such as analyzing case notes for housing instability patterns and comparing social work licensing examinations across languages. While highlighting the potential of embeddings for advancing social work research, we acknowledge limitations including information loss, training data constraints, and potential biases. We conclude that successfully implementing embedding technologies in social work requires developing domain-specific models, creating accessible tools, and establishing best practices aligned with social work's ethical principles. This integration can enhance our ability to analyze complex patterns in text data while supporting more effective services and interventions.

This review began with the modest goal of drafting a brief commentary on how the social work profession engages with and is impacted by artificial intelligence (AI). However, it quickly became apparent that a deeper exploration was required to adequately capture the profound influence of AI, one of the most transformative and debated innovations in modern history. As a result, this review evolved into an interdisciplinary endeavour, gathering seminal texts, critical articles, and influential voices from across industries and academia. This review aims to provide a comprehensive yet accessible overview, situating AI within broader societal and academic conversations as 2025 dawns. We explore perspectives from leading tech entrepreneurs, cultural icons, CEOs, and politicians alongside the pioneering contributions of AI engineers, innovators, and academics from fields as diverse as mathematics, sociology, philosophy, economics, and more. This review also briefly analyses AI's real-world impacts, ethical challenges, and implications for social work. It presents a vision for AI-facilitated simulations that could transform social work education through Advanced Personalised Simulation Training (APST). This tool uses AI to tailor high-fidelity simulations to individual student needs, providing real-time feedback and preparing them for the complexities of their future practice environments. We maintain a critical tone throughout, balancing our awe of AI's remarkable advancements with necessary caution. As AI continues to permeate every professional realm, understanding its subtleties, challenges, and opportunities becomes essential. Those who fully grasp the intricacies of this technology will be best positioned to navigate the impending AI Era.

There has been a surge in the number and type of AI tools being tested and deployed within both national and local government in the UK, including within the social care sector. Given the many ongoing and planned future developments, the time is ripe to review and reflect on the state of AI in social care. We do so by conducting semi-structured interviews with UK-based social work professionals about their experiences and opinions of past and current AI systems. Our aim is to understand what systems would practitioners like to see developed and how. We find that all our interviewees had overwhelmingly negative past experiences of technology in social care, unanimous aversion to algorithmic decision systems in particular, but also strong interest in AI applications that could allow them to spend less time on administrative tasks. In response to our findings, we offer a series of concrete recommendations, which include commitment to participatory design, as well as the necessity of regaining practitioner trust.