Which factors determine AI's propensity to support military intervention? While the use of AI in high-stakes decision-making is growing exponentially, we still lack systematic analysis of the key drivers embedded in these models. This paper conducts a conjoint experiment in which large language models (LLMs) from leading providers (OpenAI, Anthropic, Google) are asked to decide on military intervention across 128 vignettes, with each vignette run 10 times. This design enables a systematic assessment of AI decision-making in military contexts. The results are remarkably consistent across models: all models place substantial weight on the probability of success and domestic support, prioritizing these factors over civilian casualties, economic shock, or international sanctions. The paper then tests whether LLMs are sensitive to context by introducing different motivations for intervention. The scoring is indeed context-dependent; however, probability of victory remains the most important factor in all scenarios. Finally, the paper evaluates numerical sensitivity and finds that models display some responsiveness to the scale of civilian casualties but no detectable sensitivity to the size of the economic shock.

Evaluating and measuring AI Safety Level (ASL) threats are crucial for guiding stakeholders to implement safeguards that keep risks within acceptable limits. ASL-3+ models present a unique risk in their ability to uplift novice non-state actors, especially in the realm of biosecurity. Existing evaluation metrics, such as LAB-Bench, BioLP-bench, and WMDP, can reliably assess model uplift and domain knowledge. However, metrics that better contextualize "real-world risks" are needed to inform the safety case for LLMs, along with scalable, open-ended metrics to keep pace with their rapid advancements. To address both gaps, we introduce MOCET, an interpretable and doubly-scalable metric (automatable and open-ended) that can quantify real-world risks.

An entire family - a married couple and their two young sons - have been killed in an overnight Russian drone attack in Ukraine's north-eastern Sumy region, local officials have said. Regional head Oleh Hryhorov said a residential building was hit in the village of Chernechchyna. The bodies of the two children, aged four and six, and their parents were later recovered from the wreckage. Ukraine's air force said its units shot down 46 out of 65 Russian drones across the country - but there were 19 direct hits in six locations. Russia's military has not commented.

Kyiv, Ukraine – As luck would have it, emergency doctor Elina Dovzhenko was far enough from her vehicle when a Russian drone struck it, breaking the windshield and splattering pieces of shrapnel around. It was getting dark on July 9 in the bombed-out, nearly-abandoned city of Kupiansk which sits less than 5km (3 miles) from the front line in the northeastern Ukrainian region of Kharkiv – and just 40km (25 miles) west of the Russian border. But there was definitely enough light left for the Russian drone operator on the front line's opposite side to see that Dovzhenko's vehicle was a white ambulance with red stripes parked near a shelling-damaged hospital where she and her colleagues were. "We heard the drone move, it swirled and swirled around [the building], then we heard the blast," Dovzhenko, 29, told Al Jazeera. She and her colleagues were shocked and angry – but not surprised.

A total of more than 3400 public shootings have occurred in the United States between 2016 and 2022. Among these, 25.1% of them took place in an educational institution, 29.4% at the workplace including office buildings, 19.6% in retail store locations, and 13.4% in restaurants and bars. During these critical scenarios, making the right decisions while evacuating can make the difference between life and death. However, emergency evacuation is intensely stressful, which along with the lack of verifiable real-time information may lead to fatal incorrect decisions. To tackle this problem, we developed a multi-route routing optimization algorithm that determines multiple optimal safe routes for each evacuee while accounting for available capacity along the route, thus reducing the threat of crowding and bottlenecking. Overall, our algorithm reduces the total casualties by 34.16% and 53.3%, compared to our previous routing algorithm without capacity constraints and an expert-advised routing strategy respectively. Further, our approach to reduce crowding resulted in an approximate 50% reduction in occupancy in key bottlenecking nodes compared to both of the other evacuation algorithms.

Russian forces repelled four drones flying towards Moscow, the capital's mayor, Sergei Sobyanin, said in a post on Telegram. There were no initial reports of damage or casualties, Sobyanin said, adding that emergency services were working at the scene. Ukrainian forces attacked a factory in Russia's Bryansk region, destroying much of the plant, Governor Alexander Bogomaz said on Telegram. There were no casualties, Bogomaz said. Russian forces destroyed 13 Ukrainian drones overnight over Russia's Rostov, Belgorod and Bryansk regions, Moscow's Ministry of Defence said on Sunday.

The use of autonomous systems in medical evacuation (MEDEVAC) scenarios is promising, but existing implementations overlook key insights from human-robot interaction (HRI) research. Studies on human-machine teams demonstrate that human perceptions of a machine teammate are critical in governing the machine's performance. Here, we present a mixed factorial design to assess human perceptions of a MEDEVAC robot in a simulated evacuation scenario. Participants were assigned to the role of casualty (CAS) or bystander (BYS) and subjected to three within-subjects conditions based on the MEDEVAC robot's operating mode: autonomous-slow (AS), autonomous-fast (AF), and teleoperation (TO). During each trial, a MEDEVAC robot navigated an 11-meter path, acquiring a casualty and transporting them to an ambulance exchange point while avoiding an idle bystander. Following each trial, subjects completed a questionnaire measuring their emotional states, perceived safety, and social compatibility with the robot. Results indicate a consistent main effect of operating mode on reported emotional states and perceived safety. Pairwise analyses suggest that the employment of the AF operating mode negatively impacted perceptions along these dimensions. There were no persistent differences between casualty and bystander responses.

Artificial intelligence (AI) has revolutionized decision-making processes and systems throughout society and, in particular, has emerged as a significant technology in high-impact scenarios of national interest. Yet, despite AI's impressive predictive capabilities in controlled settings, it still suffers from a range of practical setbacks preventing its widespread use in various critical scenarios. In particular, it is generally unclear if a given AI system's predictions can be trusted by decision-makers in downstream applications. To address the need for more transparent, robust, and trustworthy AI systems, a suite of tools has been developed to quantify the uncertainty of AI predictions and, more generally, enable AI to "self-assess" the reliability of its predictions. In this manuscript, we categorize methods for AI self-assessment along several key dimensions and provide guidelines for selecting and designing the appropriate method for a practitioner's needs. In particular, we focus on uncertainty estimation techniques that consider the impact of self-assessment on the choices made by downstream decision-makers and on the resulting costs and benefits of decision outcomes. To demonstrate the utility of our methodology for self-assessment design, we illustrate its use for two realistic national-interest scenarios. This manuscript is a practical guide for machine learning engineers and AI system users to select the ideal self-assessment techniques for each problem.

After loudly touting the use of artificial intelligence (AI) during their 11-day conflict against Hamas in 2021, the Israel Defense Forces (IDF) have been fairly tight-lipped about the AI systems they've employed in the post-Oct. Numerous media outlets have speculated that Israel's AI platforms are being used recklessly, but Blaise Misztal, Vice President for Policy at the Jewish Institute for National Security of America (JINSA), told Fox News Digital that he believes Israel is using AI-powered drone swarms, mapping drones and targeting systems as a means to minimize civilian casualties as they seek out Hamas terrorists hiding among the populace or holed up in tunnel systems laced beneath civilian architecture. Misztal says that available evidence implies drones are a "near constant companion for ground troops as they're maneuvering through Gaza," with the IDF telling JINSA researchers that "each unit has its own mini-Air Force" supporting troop movements. A number of AI-powered drones may be mapping the underground tunnels built below Gaza, or protecting those who are traversing them as they seek out terrorists or hostages. Iris, a ground-based, throwable unit manufactured by Elbit Systems "can enter small and confined spaces, above or underground, to explore hazardous areas while relaying intelligence and reconnaissance information in real-time."

Fox News' Greg Palkot on the latest from the war in Ukraine as more weapons are sent from U.S. A massive Ukrainian drone attack on Crimea early Friday caused power cutoffs in the city of Sevastopol and set a refinery ablaze in southern Russia, Russian authorities said. The drone raids marked Kyiv's attempt to strike back during Moscow's offensive in northeastern Ukraine, which has added to the pressure on outnumbered and outgunned Ukrainian forces who are waiting for delayed deliveries of crucial weapons and ammunition from Western partners. Ukraine has not commented on the attack or claimed responsibility for it. The Russian Defense Ministry said air defenses downed 51 Ukrainian drones over Crimea, another 44 over the Krasnodar region and six over the Belgorod region. It said Russian warplanes and patrol boats also destroyed six sea drones in the Black Sea.