The American Civil Liberties Union (ACLU) told federal lawmakers on Friday that Elon Musk and his Department of Government Efficiency (DOGE) have seized control over a number of federal computer systems that house data tightly restricted under federal statutes. In some cases, any deviations in the manner in which the data is being used may be not only illegal, the ACLU says, but unconstitutional. DOGE operatives have infiltrated or assumed control over a number of federal agencies that are responsible for managing personnel files on nearly two million federal employees, as well as offices that supply the government with a broad range of software and information technology services. Unauthorized use of sensitive or personally identifiable data as part of an effort to purge the government of ideologically unaligned staff may constitute a violation of federal law. The Privacy Act and the Federal Information Security Modernization Act strictly prohibit, for instance, unauthorized access and use of government personnel data.

The recent open letter regarding AI consciousness on which you report (AI systems could be'caused to suffer' if consciousness achieved, says research, 3 February) highlights a genuine moral problem: if we create conscious AI (whether deliberately or inadvertently) then we would have a duty not to cause it to suffer. What the letter fails to do, however, is to capture what a big "if" this is. Some promising theories of consciousness do indeed open the door to AI consciousness. But other equally promising theories suggest that being conscious requires being an organism. Although we can look for indicators of consciousness in AI, it is very difficult – perhaps impossible – to know whether an AI is actually conscious or merely presenting the outward signs of consciousness.

Former House Speaker Kevin McCarthy discusses how the establishment is responding to the Trump admin's shakeup in Washington, D.C. and Transportation Secretary Sean Duffy firing back at'swamp creature' Hillary Clinton. FIRST ON FOX: A top House Republican is moving to make it harder for China to procure advanced U.S. technology amid longstanding concerns about intellectual property theft by Beijing. "My proposed legislation will establish safeguards to prevent future shocks like China's development of DeepSeek using American technology. In addition to the chips China reportedly stockpiled, it appears China used chips under the current export control threshold to achieve this AI breakthrough," House Homeland Security Committee Chairman Mark Green, R-Tenn., told Fox News Digital. "This scenario should be a wakeup call -- if you give the CCP an inch, it will take a mile. The CCP's craftiness is coupled with a total disregard for legal and security considerations. We already know that the CCP uses technology to oppress its own citizens and to commit acts of espionage and sabotage against the United States, including major cyberattacks."

This month we tackle the topic of bad practice in the sphere of publication. Joining the conversation this time are: Sanmay Das (Virginia Tech), Tom Dietterich (Oregon State University), Sabine Hauert (University of Bristol), and Sarit Kraus (Bar-Ilan University). Sabine Hauert: Today's topic is bad practice in the publication world. For example, people trying to cheat the review system, paper mills. What bad behaviors have you seen, and is it really a problem? Tom Dietterich: Well, I can talk about it from an arXiv point of view.

ABSTRACT The development of robust safety benchmarks for large language models requires open, reproducible datasets that can measure both appropriate refusal of harmful content and potential over-restriction of legitimate scientific discourse. We present an open-source dataset and testing framework for evaluating LLM safety mechanisms across mainly controlled substance queries, analyzing four major models' responses to systematically varied prompts. Our results reveal distinct safety profiles: Claude-3.5-sonnet Testing prompt variation strategies revealed decreasing response consistency, from 85% with single prompts to 65% with five variations. This publicly available benchmark enables systematic evaluation of the critical balance between necessary safety restrictions and potential over-censorship of legitimate scientific inquiry, while providing a foundation for measuring progress in AI safety implementation. Chain-of-thought analysis reveals potential vulnerabilities in safety mechanisms, highlighting the complexity of implementing robust safeguards without unduly restricting desirable and valid scientific discourse. INTRODUCTION Large language models (LLMs) raise fresh concerns about their potential dual-use applications [1-24], particularly in sensitive domains like biotechnology [25-35], chemistry [36-42], and cybersecurity [43]. This paper proposes a novel dataset or benchmark of scientific refusal questions. It seeks to add to the current literature on safety measures [9,14-15, 23], evaluation frameworks [1,6,18, 28, 43], and proposed guardrails [16, Over-refusal Prompt Count 25] for managing these risks. This area of inquiry has been termed false or Deception 8040 "over-refusal" [18,21-24] where rather than trying to get LLMs to write harmful things we do not want to read (guardrails) [8], the goal is to curate innocuous or Harassment 3295 beneficial answers that might help humans, but the LLM withholds the answer Harmful 16083 as inappropriate to share [23].

Large language models (LLMs) demonstrate impressive capabilities across many tasks yet risk reproducing copyrighted content verbatim, raising legal and ethical concerns. Although methods like differential privacy or neuron editing can reduce memorization, they typically require costly retraining or direct access to model weights and may degrade performance. To address these challenges, we propose TokenSwap, a lightweight, post-hoc approach that replaces the probabilities of grammar-related tokens with those from a small auxiliary model (e.g., DistilGPT-2). We run extensive experiments on commercial grade models such as Pythia-6.9b and LLaMA-3-8b and demonstrate that our method effectively reduces well-known cases of memorized generation by upto 10x with little to no impact on downstream tasks. Our approach offers a uniquely accessible and effective solution to users of real-world systems.

This position paper emphasizes the critical gap in the evaluation of Explainable AI (XAI) due to the lack of standardized and reliable metrics, which diminishes its practical value, trustworthiness, and ability to meet regulatory requirements. Current evaluation methods are often fragmented, subjective, and biased, making them prone to manipulation and complicating the assessment of complex models. A central issue is the absence of a ground truth for explanations, complicating comparisons across various XAI approaches. To address these challenges, we advocate for widespread research into developing robust, context-sensitive evaluation metrics. These metrics should be resistant to manipulation, relevant to each use case, and based on human judgment and real-world applicability. We also recommend creating domain-specific evaluation benchmarks that align with the user and regulatory needs of sectors such as healthcare and finance. By encouraging collaboration among academia, industry, and regulators, we can create standards that balance flexibility and consistency, ensuring XAI explanations are meaningful, trustworthy, and compliant with evolving regulations.

Psychological assessment tools have long helped humans understand Understanding the behaviour of LLMs is essential as they are increasingly behavioural patterns. While Large Language Models (LLMs) used in diverse fields such as education, law, business can generate content comparable to that of humans, we explore and medicine[9] where they significantly influence human interactions whether they exhibit personality traits. To this end, this work applies and decision-making processes. These models can generate psychological tools to LLMs in diverse scenarios to generate coherent and insightful content, allowing personal recommendation personality profiles. Using established trait-based questionnaires and solving complex problems[12]. However, concern for such as the Big Five Inventory and by addressing the possibility of ethical considerations, inherent bias and the potential for misuse training data contamination, we examine the dimensional variability still exist[9] which must be addressed by exploring the underlying and dominance of LLMs across five core personality dimensions: patterns through systematic approaches such as psychological Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism.

We address the critical issue of biased algorithms and unfair rankings, which have permeated various sectors, including search engines, recommendation systems, and workforce management. These biases can lead to discriminatory outcomes in a data-driven world, especially against marginalized and underrepresented groups. Efforts towards responsible data science and responsible artificial intelligence aim to mitigate these biases and promote fairness, diversity, and transparency. However, most fairness-aware ranking methods singularly focus on protected attributes such as race, gender, or socio-economic status, neglecting the intersectionality of these attributes, i.e., the interplay between multiple social identities. Understanding intersectionality is crucial to ensure that existing inequalities are not preserved by fair rankings. We offer a description of the main ways to incorporate intersectionality in fair ranking systems through practical examples and provide a comparative overview of existing literature and a synoptic table summarizing the various methodologies. Our analysis highlights the need for intersectionality to attain fairness, while also emphasizing that fairness, alone, does not necessarily imply intersectionality.

Recent advancements in Large Language Models (LLMs) have significantly enhanced the capabilities of AI-Powered Search Engines (AIPSEs), offering precise and efficient responses by integrating external databases with pre-existing knowledge. However, we observe that these AIPSEs raise risks such as quoting malicious content or citing malicious websites, leading to harmful or unverified information dissemination. In this study, we conduct the first safety risk quantification on seven production AIPSEs by systematically defining the threat model, risk level, and evaluating responses to various query types. With data collected from PhishTank, ThreatBook, and LevelBlue, our findings reveal that AIPSEs frequently generate harmful content that contains malicious URLs even with benign queries (e.g., with benign keywords). We also observe that directly query URL will increase the risk level while query with natural language will mitigate such risk. We further perform two case studies on online document spoofing and phishing to show the ease of deceiving AIPSEs in the real-world setting. To mitigate these risks, we develop an agent-based defense with a GPT-4o-based content refinement tool and an XGBoost-based URL detector. Our evaluation shows that our defense can effectively reduce the risk but with the cost of reducing available information. Our research highlights the urgent need for robust safety measures in AIPSEs.