AI alerts have played and continue to play a critical role in detecting and controlling future outbreaks like COVID-19. Did you know that artificial intelligence (AI) technology first sounded the alarm on COVID-19? An algorithm developed by BlueDot, a Canadian AI firm, scoured news reports and airline ticketing data to detect the outbreak on Dec. 31, 2019 in China. On the same day, HealthMap, a Boston Children's Hospital website using AI, spotted a news report of a new type of pneumonia in Wuhan, China, and alerted global health officials. HealthMap was also the first to notify Chinese health officials that COVID-19 was expanding outside of China.

Many real-world situations of ethical relevance, in particular those of large-scale social choice such as mitigating climate change, involve not only many agents whose decisions interact in complicated ways, but also various forms of uncertainty, including quantifiable risk and unquantifiable ambiguity. In such problems, an assessment of individual and groupwise moral responsibility for ethically undesired outcomes or their responsibility to avoid such is challenging and prone to the risk of under- or overdetermination of responsibility. In contrast to existing approaches based on strict causation or certain deontic logics that focus on a binary classification of `responsible' vs `not responsible', we here present several different quantitative responsibility metrics that assess responsibility degrees in units of probability. For this, we use a framework based on an adapted version of extensive-form game trees and an axiomatic approach that specifies a number of potentially desirable properties of such metrics, and then test the developed candidate metrics by their application to a number of paradigmatic social choice situations. We find that while most properties one might desire of such responsibility metrics can be fulfilled by some variant, an optimal metric that clearly outperforms others has yet to be found.

Robustness of machine learning models to various adversarial and non-adversarial corruptions continues to be of interest. In this paper, we introduce the notion of the boundary thickness of a classifier, and we describe its connection with and usefulness for model robustness. Thick decision boundaries lead to improved performance, while thin decision boundaries lead to overfitting (e.g., measured by the robust generalization gap between training and testing) and lower robustness. We show that a thicker boundary helps improve robustness against adversarial examples (e.g., improving the robust test accuracy of adversarial training) as well as so-called out-of-distribution (OOD) transforms, and we show that many commonly-used regularization and data augmentation procedures can increase boundary thickness. On the theoretical side, we establish that maximizing boundary thickness during training is akin to the so-called mixup training. Using these observations, we show that noise-augmentation on mixup training further increases boundary thickness, thereby combating vulnerability to various forms of adversarial attacks and OOD transforms. We can also show that the performance improvement in several lines of recent work happens in conjunction with a thicker boundary.

Machine learning algorithms are used in finance, medicine, and criminal justice, and therefore they can have a deep The advent of machine learning techniques has impact on society. With the recent success of AI applications made it possible to obtain predictive systems that in the private and public domain, legal professionals are now have overturned traditional legal practices. However, interested in artificial intelligence, especially since many rather than leading to systems seeking to startups disrupt the legal market space by seeking to benefit replace humans, the search for the determinants of these new AI techniques (Bex et al., 2017). in a court decision makes it possible to give a However, the arrival of these new techniques has brought better understanding of the decision mechanisms a number of ethical issues. Firstly, machine learning and carried out by the judge. By using a large amount data mining techniques are capable of exploiting personal of court decisions in matters of divorce produced and legal data that are more and more easily accessible on by French jurisdictions and by looking at the variables the Internet, leading to questions about privacy preserving, that allow to allocate an alimony or not, and or even attacks on democracy (Wylie, 2019). Secondly, to define its amount, we seek to identify if there artificial intelligence programs reason in a simplistic way, may be extralegal factors in the decisions taken but the real world is complex, especially in the legal field by the judges. From this perspective, we present which leaves a certain part to the human interpretation of an explainable AI model designed in this purpose the law and characterization of the fact. A machine learning by combining a classification with random forest program has great difficulty in dealing with the unexpected and a regression model, as a complementary tool events that happen in the real world. Intelligent system to existing decision-making scales or guidelines algorithms are black boxes that are impossible to understand, created by practitioners.

AI startup Trefos is helping foresters see the wood for the trees. Using custom lidar and camera-mounted drones, the Philadelphia-based company collects data for high-resolution, 3D forest maps. These metrics allow government agencies and the forestry industry to estimate the volume of timber and biomass in an area of forest, as well as the amount of carbon stored in the trees. With this unprecedented detail, foresters can make more informed decisions when, for example, evaluating the need for controlled burns to clear biomass and reduce the risk of wildfires. "Forests are often very dense, with a very repetitive layout," said Steven Chen, founder and CEO of the startup, a member of the NVIDIA Inception program, which supports startups from product development to deployment. "We can use deep learning algorithms to detect trees, isolate them from the surrounding branches and vines, and use those as landmarks."

Explainable machine learning is a sub-discipline of artificial intelligence (AI) and machine learning that attempts to summarize how machine learning systems make decisions. Summarizing how machine learning systems make decisions can be helpful for a lot of reasons, like finding data-driven insights, uncovering problems in machine learning systems, facilitating regulatory compliance, and enabling users to appeal -- or operators to override -- inevitable wrong decisions. Of course all that sounds great, but explainable machine learning is not yet a perfect science. Figure 1: Explanations created by H2O Driverless AI. These explanations are probably better suited for data scientists than for business users.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The U.S. Army wants future armored vehicles to instantly make decisions about terrain navigation, target identification, incoming enemy fire, force positions and warfare strategy. In fact, the military wants this to happen in a matter of seconds and all without every nuance needing to be controlled or micromanaged by humans. It is a known and often discussed concept, rapidly gaining traction as new technology continues to emerge at rocket speed.

The United States lashed out on Wednesday at a United Nations probe into the American drone attack that killed a top Iranian general, saying it gave "a pass to terrorists". US President Donald Trump ordered the killing of Iran's General Qassem Soleimani in a January attack near Baghdad's international airport. The incident stoked fears of an all-out conflict between Iran and the US. The US air raid that killed Soleimani and others in his convoy was "unlawful" and an "arbitrary killing" that violated the UN charter, the UN expert on extrajudicial killings, Agnes Callamard, concluded in a report on Tuesday. She said the US provided no evidence "an imminent attack" against American interests was being planned and, therefore, its "self-defence" justification did not apply.

Since the death of George Floyd on May 25, Americans have taken to the streets to peacefully protest in unprecedented numbers, calling for an end to our national culture of racism and police brutality. These protests have, on too many occasions, been met with violent force from police, who have been caught on camera using tear gas, pepper spray, rubber bullets, and other supposedly less-lethal weapons against unarmed and compliant people. Police around the country are also devoting considerable time and energy to collecting intelligence on protesters and protest movements, with methods ranging from monitoring social media posts to aerial surveillance--sometimes, with drones. Police, military, and federal government forces have regularly flown surveillance helicopters and small, crewed surveillance aircraft over protest areas, capturing real-time video and photographs of protest movements. The New York Times found that by mid-June, the Department of Homeland Security had captured more than 270 hours of surveillance footage of protests from helicopters, airplanes, and drones, data that was shared with a digital network accessible by other federal agencies and by police departments.

Radiology Experts Warn that Ensuring Autonomous AI Safety and Effectiveness is "A Long Way Off" Imaging artificial intelligence (AI) solutions that provide decision support or administrative assistance to radiologists are poised to improve patient care. However, regulators are currently unable to ensure safety and effectiveness of AI intended to automate key components of imaging workflow without physician-expert oversight. That is the message that the American College of Radiology (ACR) and Radiological Society of North America (RSNA) sent to the U.S. Food and Drug Administration (FDA) regarding the agency's February 2020 public workshop on the "Evolving Role of Artificial Intelligence in Radiological Imaging," which focused on higher risk, "autonomously" functioning AI. Examples of autonomous imaging AI would include algorithms that identify normal radiological examinations or rule out critical diseases. "It is unlikely FDA could provide reasonable assurance of the safety and effectiveness of autonomous AI in radiology patient care without more rigorous testing, surveillance, and other oversight mechanisms throughout the total product life cycle," the joint letter states.