The COVID-19 pandemic has devastated many low- and middle-income countries, causing widespread food insecurity and a sharp decline in living standards1. In response to this crisis, governments and humanitarian organizations worldwide have distributed social assistance to more than 1.5 billion people2. Targeting is a central challenge in administering these programmes: it remains a difficult task to rapidly identify those with the greatest need given available data3,4. Here we show that data from mobile phone networks can improve the targeting of humanitarian assistance. Our approach uses traditional survey data to train machine-learning algorithms to recognize patterns of poverty in mobile phone data; the trained algorithms can then prioritize aid to the poorest mobile subscribers. We evaluate this approach by studying a flagship emergency cash transfer program in Togo, which used these algorithms to disburse millions of US dollars worth of COVID-19 relief aid. Our analysis compares outcomes—including exclusion errors, total social welfare and measures of fairness—under different targeting regimes. Relative to the geographic targeting options considered by the Government of Togo, the machine-learning approach reduces errors of exclusion by 4–21%. Relative to methods requiring a comprehensive social registry (a hypothetical exercise; no such registry exists in Togo), the machine-learning approach increases exclusion errors by 9–35%. These results highlight the potential for new data sources to complement traditional methods for targeting humanitarian assistance, particularly in crisis settings in which traditional data are missing or out of date. Machine-learning algorithms can take advantage of survey and mobile phone data to help to identify people most in need of aid, complementing traditional methods for targeting humanitarian assistance.

There is widespread agreement that responsible artificial intelligence requires principles such as fairness, transparency, privacy, human safety, and explainability. Nearly all ethicists and tech policy advocates stress these factors and push for algorithms that are fair, transparent, safe, and understandable.1 But it is not always clear how to operationalize these broad principles or how to handle situations where there are conflicts between competing goals.2 It is not easy to move from the abstract to the concrete in developing algorithms and sometimes a focus on one goal comes at the detriment of alternative objectives.3 In the criminal justice area, for example, Richard Berk and colleagues argue that there are many kinds of fairness and it is "impossible to maximize accuracy and fairness at the same time, and impossible simultaneously to satisfy all kinds of fairness."4

Radiology artificial intelligence company Aidoc scored FDA 510(k) clearance for its tool for flagging and triaging cases of pneumothorax, or a collapsed lung, on X-rays. Aidoc said the software could run on all X-rays, including portable machines, and automatically notes positive cases of pneumothorax so physicians can focus on these images more quickly. Some of Aidoc's other FDA-cleared tools include software for triaging and notification of incidental pulmonary embolism, triaging cervical spine fractures and flagging acute intracranial hemorrhage. "We're very excited about this important milestone," CEO Elad Walach said in a statement. "This FDA clearance further validates the breadth of our AI platform, going beyond specific AI algorithms to act as a healthcare AI hub for the enterprise's cross-specialty needs. This includes ER, ICU, outpatient centers, inpatient admissions, and the coordination of care and communication among providers. By bringing radiologists and proceduralists to the same AI platform, we enable enhanced collaboration across departments and systems to deliver patients with the right treatment at the right time."

Advanced technologies, especially artificial intelligence (AI), are leveraged by many companies in various industries to help fuel business growth, achieve efficiencies and support human workers. Organizations that implement AI solutions tend to benefit from enhanced performance due to the plethora of opportunities and applications, and this shouldn't come as a surprise. AI has seeped into daily life, from digital assistants to the technology that powers our smartphones. It's expected that law enforcement agencies nationwide will continue to adopt AI-powered tools to serve various purposes. How are these organizations using AI currently?

Modern military operations, whether it be combat, medical or disaster relief, require complex decisions to be made very quickly, and AI could be used to make them. The Defense Advanced Research Projects Agency (DARPA) launched a new program aimed at introducing artificial intelligence into the decision making process. This is because, in a real world emergency situation, that might require instant choices between who does and doesn't get help, the answer isn't always clear and people disagree over the correct course of action - AI will make a quick decision. The latest DARPA initiative, called'In the Moment', will involve new technology that could take difficult decisions in stressful situations, using live analysis of data, such as the condition of patients in a mass-casualty event and drug availability. It comes as the U.S. military increasingly leans on technology to reduce human error, with DARPA arguing removing human bias from decision making will'save lives'.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Liberal magazine The New Republic (TNR) garnered the scorn of critics after publishing an article Friday celebrating "petromasculinity" being rejected by younger generations, specifically those using online dating apps. In the piece, headlined "'Petromasculinity' Is Becoming Toxic, Too--at Least to Online Daters," TNR praised what appeared to be a shift in online daters preferring a potential partner who cares about climate change and "rejecting petromasculinity: the climate denial, authoritarian politics, and sexism that are too often inextricably linked." The dating app Tinder is shown on an Apple iPhone in this photo illustration taken February 10, 2016.

To read the full story, subscribe or sign in. Biobeat Technologies Ltd. reported its remote patient monitoring system received FDA clearance to monitor respiratory rate and body temperature. The wireless chest and wrist monitoring device is already cleared for cuffless blood pressure monitoring, blood oxygen saturation and pulse rate. The artificial intelligence platform utilizes a photoplethysmography-based sensor at the surface of the skin that measures volumetric variations of blood circulation.

With Russia's invasion of Ukraine as the backdrop, the United Nations recently held a meeting to discuss the use of autonomous weapons systems, commonly referred to as killer robots. These are essentially weapons that are programmed to find a class of targets, then select and attack a specific person or object within that class, with little human control over the decisions that are made. Russia took center stage in this discussion, in part because of its potential capabilities in this space, but also because its diplomats thwarted the effort to discuss these weapons, saying sanctions made it impossible to properly participate. For a discussion that to date had been far too slow, Russia's spoiling slowed it down even further. I have been tracking the development of autonomous weapons and attending the UN discussions on the issue for over seven years, and Russia's aggression is becoming an unfortunate test case for how artificial intelligence (AI)–fueled warfare can and likely will proceed.

Aidoc, the leading provider of healthcare AI solutions, today announced that it received FDA 510(k) clearance for its triage and notification of pneumothorax on X-ray exams. A one-stop partner for the enterprise's clinical AI needs, Aidoc's other seven FDA-cleared solutions are already implemented across U.S. health systems, flagging and communicating suspected pathologies in CT exams – and now have expanded to the high volume X-ray modality. Aidoc's newly FDA-cleared solution runs on all X-ray machines including portable ones, and is designed to analyze X-ray images. It automatically flags positive cases of pneumothorax, facilitating physicians to read X-rays in a timely manner. The ability to quickly identify pneumothorax is imperative as it can worsen rapidly and result in respiratory or cardiac failure.

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: A.G. was funded by the National Science Foundation (DBI 1553206) and National Institutes of Health (R01GM135631). C.S.G. was funded by the National Institutes of Health (R01 HG010067) and the Gordon and Betty Moore Foundation (GBMF 4552). S.R. was funded by the Wisconsin Alumni Foundation (AAD5912). F.M. was funded by the Donald Hill Family Fellowship.