Counterfactuals, or modified inputs that lead to a different outcome, are an important tool for understanding the logic used by machine learning classifiers and how to change an undesirable classification. Even if a counterfactual changes a classifier's decision, however, it may not affect the true underlying class probabilities, i.e. the counterfactual may act like an adversarial attack and ``fool'' the classifier. We propose a new framework for creating modified inputs that change the true underlying probabilities in a beneficial way which we call Trustworthy Actionable Perturbations (TAP). This includes a novel verification procedure to ensure that TAP change the true class probabilities instead of acting adversarially. Our framework also includes new cost, reward, and goal definitions that are better suited to effectuating change in the real world. We present PAC-learnability results for our verification procedure and theoretically analyze our new method for measuring reward. We also develop a methodology for creating TAP and compare our results to those achieved by previous counterfactual methods.

Ukraine launched its largest-ever kamikaze drone attack on Russia while Russian President Vladimir Putin visited China, killing two people and causing an oil refinery fire in the Black Sea, according to officials. "Fifty-one UAVs were destroyed and intercepted over Crimea, 44 over the Krasnodar region, six over the Belgorod region and one over Kursk region," Russia's military said in a press release according to Voice of America. The wave of drones attacked several targets around the Belgorod region and along the coast of the Black Sea. Belgorod Governor Vyacheslav Gladkov said a mother and child were killed while traveling in a car, and authorities managed to extinguish the fire at the Tuapse refinery. "The child was in critical condition. Doctors did everything possible to save him," Gladkov said.

Russian guided bombs have killed at least two people and injured 13 in Ukraine's northeastern city of Kharkiv, local officials say, as Russia continues its major military offensive in the region. It was not immediately clear what the bombs had been targeting on Friday, but the regional governor said those injured were civilians. "Among the 13 wounded, four are in a serious condition," Governor Oleh Syniehubov said on the Telegram messaging app. Kharkiv, Ukraine's second largest city, and the surrounding region have long been targeted by Russian attacks but the strikes have become more intense in recent months, hitting civilian and energy infrastructure. Reporting from Kharkiv on Friday, Al Jazeera's John Holman said several strikes were heard and a "thick, black plume of smoke" was visible. "We don't know yet what's been hit – if it's factories or residential infrastructure," he reported, adding that the city had also experienced drone attacks.

On 7 and 8 May in Washington DC, the city's biggest convention hall welcomed America's military industrial complex, its top technology companies, and its most outspoken justifiers of war crimes. Of course, that's not how they would describe it. It was the inaugural "AI Expo for National Competitiveness", hosted by the Special Competitive Studies Project – better known as the "techno-economic" thinktank created by the former Google CEO and current billionaire Eric Schmidt. The conference's lead sponsor was Palantir, a software company co-founded by Peter Thiel that's best known for inspiring 2019 protests against its work with Immigration and Customs Enforcement (Ice) at the height of Trump's family separation policy. Currently, Palantir is supplying some of its AI products to the Israel Defense Forces. The conference hall was also filled with booths representing the US military and dozens of its contractors, ranging from Booz Allen Hamilton to a random company that was described to me as Uber for airplane software.

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.

Fox News chief national security correspondent Jennifer Griffin reports on how technology is revolutionizing modern warfare on'Special Report.' Modern warfare is changing rapidly, and harnessing artificial intelligence is key to staying ahead of America's adversaries. Software companies including Govini and Palantir are behind the production and modernization of today's most high-tech weapon systems. Both companies were at the second annual AI Expo for National Competitiveness in Washington to showcase their work to the nation's top military brass. Fox News saw first-hand this cutting-edge technology and had an exclusive interview with Palantir's CEO and co-founder Alex Karp, whose software is being used in Ukraine and the Middle East.

Visiting Kharkiv, Ukrainian President Volodymyr Zelenskyy said the situation in the northeast was "extremely difficult" but "under control" after the military partially halted a Russian advance, most notably thwarting an invasion of Vovchansk, 5km (3 miles) from the border with Russia. Sergiy Bolvinov, the head of police investigations in Ukraine's northeastern Kharkiv region, accused Russia of taking "30 to 40" civilians captive in Vovchansk to use as "human shields" near their command centre. General Christopher Cavoli, NATO's supreme allied commander in Europe, said he did not believe Russia's military had the troop numbers to make a strategic breakthrough in the Kharkiv region and he was confident Ukrainian forces would hold their lines there. Ukraine's General Staff said Russia was directing its most intense assaults on the front line near the cities of Pokrovsk and Kramatorsk in the eastern Donetsk region, where Russia's offensive has been unrelenting for months. An air raid alert in the northeastern Kharkiv region remained in place for more than 16 and a half hours amid Russian drone and missile attacks.

The COVID-19 Forecast Hub was founded in 2020 and serves as a "central repository of COVID-19 forecasts from over 50 independent research groups" [1]. Participant research groups submit county, state and national US COVID-19 forecasts with a standardized format; and the Forecast Hub provides an interactive visualization tool to help decision makers and the general public analyze weekly predictions for COVID-19 hospitalizations, cases and deaths. The standardized predictions collected from all research groups, as well as the predictions for an ensemble model that brings all individual predictions together, are also shared with the Centers for Disease Control and Prevention (CDC) who uses these results for their official COVID-19 communications [2]. The COVID-19 Forecast Hub has been, and continues to be, a critical centralized resource to promote transparent decision making. Nevertheless, by focusing exclusively on prediction accuracy at different spatial granularities (e.g., county or state), the Forecast Hub fails to evaluate whether the proposed models are fair i.e., share similar prediction performance across social determinants that have been known to play a role in COVID-19 including race, ethnicity and rurality [3, 4]. Diverse prediction performance across social determinants - for example, higher prediction errors for a given minority race or ethnicity - could negatively impact resource allocation and intervention decisions e.g., hospital beds or stay-at-home orders, given that the CDC appears to be using the Forecast Hub predictions for official communications that subsequently inform policy decisions [2]. In other words, allocation or intervention harms might occur if models from the Forecast Hub are used to inform decision making across communities without taking into account fairness metrics [5]. There are many reasons why the COVID-19 prediction performance can be different across social determinants such as race, ethnicity or urbanization levels. The Forecast Hub's COVID-19 prediction models are trained on datasets containing COVID-19

The deployment of AI systems for welfare benefit allocation allows for accelerated decision-making and faster provision of critical help, but has already led to an increase in unfair benefit denials and false fraud accusations. Collecting data in the US and the UK (N = 2449), we explore the public acceptability of such speed-accuracy trade-offs in populations of claimants and non-claimants. We observe a general willingness to trade off speed gains for modest accuracy losses, but this aggregate view masks notable divergences between claimants and non-claimants. Although welfare claimants comprise a relatively small proportion of the general population (e.g., 20% in the US representative sample), this vulnerable group is much less willing to accept AI deployed in welfare systems, raising concerns that solely using aggregate data for calibration could lead to policies misaligned with stakeholder preferences. Our study further uncovers asymmetric insights between claimants and non-claimants. The latter consistently overestimate claimant willingness to accept speed-accuracy trade-offs, even when financially incentivized for accurate perspective-taking. This suggests that policy decisions influenced by the dominant voice of non-claimants, however well-intentioned, may neglect the actual preferences of those directly affected by welfare AI systems. Our findings underline the need for stakeholder engagement and transparent communication in the design and deployment of these systems, particularly in contexts marked by power imbalances.

Drought is a complex environmental phenomenon that affects millions of people and communities all over the globe and is too elusive to be accurately predicted. This is mostly due to the scalability and variability of the web of environmental parameters that directly/indirectly causes the onset of different categories of drought. Since the dawn of man, efforts have been made to uniquely understand the natural indicators that provide signs of likely environmental events. These indicators/signs in the form of indigenous knowledge system have been used for generations. The intricate complexity of drought has, however, always been a major stumbling block for accurate drought prediction and forecasting systems. Recently, scientists in the field of agriculture and environmental monitoring have been discussing the integration of indigenous knowledge and scientific knowledge for a more accurate environmental forecasting system in order to incorporate diverse environmental information for a reliable drought forecast. Hence, in this research, the core objective is the development of a semantics-based data integration middleware that encompasses and integrates heterogeneous data models of local indigenous knowledge and sensor data towards an accurate drought forecasting system for the study areas. The local indigenous knowledge on drought gathered from the domain experts is transformed into rules to be used for performing deductive inference in conjunction with sensors data for determining the onset of drought through an automated inference generation module of the middleware. The semantic middleware incorporates, inter alia, a distributed architecture that consists of a streaming data processing engine based on Apache Kafka for real-time stream processing; a rule-based reasoning module; an ontology module for semantic representation of the knowledge bases.