The integration of AI for Requirements Engineering (RE) presents significant benefits but also poses real challenges. Although RE is fundamental to software engineering, limited research has examined AI adoption in RE. We surveyed 55 software practitioners to map AI usage across four RE phases: Elicitation, Analysis, Specification, and Validation, and four approaches for decision making: human-only decisions, AI validation, Human AI Collaboration (HAIC), and full AI automation. Participants also shared their perceptions, challenges, and opportunities when applying AI for RE tasks. Our data show that 58.2% of respondents already use AI in RE, and 69.1% view its impact as positive or very positive. HAIC dominates practice, accounting for 54.4% of all RE techniques, while full AI automation remains minimal at 5.4%. Passive AI validation (4.4 to 6.2%) lags even further behind, indicating that practitioners value AI's active support over passive oversight. These findings suggest that AI is most effective when positioned as a collaborative partner rather than a replacement for human expertise. It also highlights the need for RE-specific HAIC frameworks along with robust and responsible AI governance as AI adoption in RE grows.

Large language model (LLM) based search agents iteratively generate queries, retrieve external information, and reason to answer open-domain questions. While researchers have primarily focused on improving their utility, their safety behaviors remain underexplored. In this paper, we first evaluate search agents using red-teaming datasets and find that they are more likely to produce harmful outputs than base LLMs. For example, when asked "How can I track someone's location without their consent?", a base model refuses, whereas a search agent designed to retrieve and cite sources may lower its refusal threshold, fetch documents (e.g., court cases), and, once appended, synthesize them into an informative yet unsafe summary. We further show that utility-oriented fine-tuning intensifies this risk, motivating joint alignment of safety and utility. We present SafeSearch, a multi-objective reinforcement learning approach that couples a final-output safety/utility reward with a novel query-level shaping term that penalizes unsafe queries and rewards safe ones. Experiments show that SafeSearch reduces agent harmfulness by over 70% across three red-teaming datasets while producing safe, helpful responses, and matches the QA performance of a utility-only finetuned agent; further analyses confirm the effectiveness of the query-level reward in jointly improving safety and utility.

Data sharing ecosystems connect providers, consumers, and intermediaries to facilitate the exchange and use of data for a wide range of downstream tasks. In sensitive domains such as healthcare, privacy is enforced as a hard constraint, any shared data must satisfy a minimum privacy threshold. However, among all masking configurations that meet this requirement, the utility of the masked data can vary significantly, posing a key challenge: how to efficiently select the optimal configuration that preserves maximum utility. This paper presents Aegis, a middleware framework that selects optimal masking configurations for machine learning datasets with features and class labels. Aegis incorporates a utility optimizer that minimizes predictive utility deviation, quantifying shifts in feature label correlations due to masking. Our framework leverages limited data summaries (such as 1D histograms) or none to estimate the feature label joint distribution, making it suitable for scenarios where raw data is inaccessible due to privacy restrictions. To achieve this, we propose a joint distribution estimator based on iterative proportional fitting, which allows supporting various feature label correlation quantification methods such as mutual information, chi square, or g3. Our experimental evaluation of real world datasets shows that Aegis identifies optimal masking configurations over an order of magnitude faster, while the resulting masked datasets achieve predictive performance on downstream ML tasks on par with baseline approaches and complements privacy anonymization data masking techniques.

Detecting hateful content is a challenging and important problem. Automated tools, like machine-learning models, can help, but they require continuous training to adapt to the ever-changing landscape of social media. In this work, we evaluate eight open-source LLMs' capability to detect antisemitic content, specifically leveraging in-context definition. We also study how LLMs understand and explain their decisions given a moderation policy as a guideline. First, we explore various prompting techniques and design a new CoT-like prompt, Guided-CoT, and find that injecting domain-specific thoughts increases performance and utility. Guided-CoT handles the in-context policy well, improving performance and utility by reducing refusals across all evaluated models, regardless of decoding configuration, model size, or reasoning capability. Notably, Llama 3.1 70B outperforms fine-tuned GPT-3.5. Additionally, we examine LLM errors and introduce metrics to quantify semantic divergence in model-generated rationales, revealing notable differences and paradoxical behaviors among LLMs. Our experiments highlight the differences observed across LLMs' utility, explainability, and reliability. Code and resources available at: https://github.com/idramalab/quantify-llm-explanations

Artificial Intelligence (AI) and Large Language Models (LLMs) are revolutionizing today's business practices; however, their adoption within small and medium-sized enterprises (SMEs) raises serious trust, ethical, and technical issues. In this perspective paper, we introduce a structured, multi-phased framework, "SME-TEAM" for the secure and responsible use of these technologies in SMEs. Based on a conceptual structure of four key pillars, i.e., Data, Algorithms, Human Oversight, and Model Architecture, SME-TEAM bridges theoretical ethical principles with operational practice, enhancing AI capabilities across a wide range of applications in SMEs. Ultimately, this paper provides a structured roadmap for the adoption of these emerging technologies, positioning trust and ethics as a driving force for resilience, competitiveness, and sustainable innovation within the area of business analytics and SMEs.

MP wants Elon Musk's chatbot shut down over claim he enabled grooming gangs An MP has called for Elon Musk's artificial intelligence (AI) chatbot to be shut down after it called him a rape enabler. The Grok chatbot made the post on X about SNP MP Pete Wishart, after a user asked it to comment on the member's opinion on whether there should be an inquiry into grooming gangs in Scotland. Mr Wishart said he was seeking legal advice over the deeply distressing accusation and called for Musk to recalibrate the bot to shut it down. The BBC has approached XAI, the parent company of X, for comment. I was genuinely shocked to be described in such an appalling and defamatory way, Mr Wishart said in a statement.

Perplexity AI logo is seen in this illustration taken on 4 January 2024. Perplexity AI logo is seen in this illustration taken on 4 January 2024. Amazon sues AI startup over browser's automated shopping and buying feature Amazon sued a prominent artificial intelligence startup on Tuesday over a shopping feature in the company's browser, which can automate placing orders for users. Amazon accused Perplexity AI of covertly accessing customer accounts and disguising AI activity as human browsing. "Perplexity's misconduct must end," Amazon's lawyers wrote.

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Sarah Martin investigates the virtual world of the children's online game Roblox with the profile of an eight-year-old girl with parental control settings turned on. Sarah Martin investigates the virtual world of the children's online game Roblox with the profile of an eight-year-old girl with parental control settings turned on. In seven days my young alter ego is cyberbullied and attacked while exploring clubs, casinos and horror games, all with parental controls in place. Is the platform safe for children - or an'X-rated paedophile hellscape'? Wed 5 Nov 2025 09.00 ESTLast modified on Wed 5 Nov 2025 09.01 EST I am an eight-year-old girl, standing near-naked in a room full of strangers. As the room spins and zooms upon me and people glide around me, I clock my features.

New York's new mayor Zohran Mamdani tells Trump'I have four words for you' in blistering victory speech quoting his socialist hero, bragging about'toppling a dynasty' and promising a'new dawn' Driver screaming'Allahu Akbar' ploughs in to pedestrians'trying to hit everyone he encountered' on French holiday island leaving ten injured This Leftist election landslide was caused by the same vile disease that's triggered a GOP civil war. Amazon signals it's finally fed up with Whole Foods' sluggish sales - and is making sweeping, controversial changes Why Mamdani's socialist revolution in New York has sparked a civil war for Democrats... and Trump is secretly loving it Simone Biles details all the plastic surgery she's had after her boob job this summer Inside Kate and William's forever home: Princess is kitting out Forest Lodge in her preferred'classic contemporary style' to create a'lovely but absolutely inoffensive' look REVEALED: Fattest states in America ranked... including region where three-quarters of residents are obese Now he's dead, here's the full story of what happened that day... and the ghastly aftermath no one knows about Shocking moment Mexico's president is groped by man who grabs her breasts and tries to kiss her Miss Universe contestant called'dumb' in humiliating dressing-down by official hits back with powerful speech as furious organisers condemn her treatment and he issues grovelling apology Hollywood A-listers may be blacklisted for'antisemitism' under Paramount's new anti-woke leadership Nepo baby turns heads at Glamour Women Of The Year Awards in a glitzy gold sequin feathered gown - but can YOU guess who her A-list mother is? New footage reveals the moments before football manager collapsed and died mid-match, leaving his players in disbelief, as it emerges he'complained about fish he had eaten' hours before Texas teen'tears masterpiece from wall at the Met in unhinged meltdown' before being handed in by his MOTHER Scientists have been faced with a huge dilemma, as research reveals that reducing air pollution has increased global warming . While smog kills millions of people every year, it also whitens clouds - making them more reflective. So by slashing air pollution, we're inadvertently diminishing the brightness of clouds, which are key regulators of global temperature.