Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A former Air Force intelligence analyst pleaded guilty Wednesday to leaking classified documents to a reporter about military drone strikes against al-Qaida and other terrorist targets. The guilty plea from Daniel Hale, 33, of Nashville, Tennessee, comes just days before he was slated to go on trial in federal court in Alexandria, Virginia, for violating the World War I-era Espionage Act. Hale admitted leaking roughly a dozen secret and top-secret documents to a reporter in 2014 and 2015, when he was working for a contractor as an analyst at the National Geospatial-Intelligence Agency (NGA).

Businesses are increasingly adopting technology to stay ahead in the race. The world has brought us to a critical spot where you should either embrace artificial intelligence and its applications or accept the fall. Artificial intelligence, by which computers are programmed to mimic intelligent learning behavior, is finding solutions for many real-time issues. By implying such futuristic technology in industry flow is opening the door for'industrial AI.' Industrial AI provides significant insights by combining data science and machine learning with the domain of expertise to deliver for purpose, industrial applications to drive sustainable business values. While some people take artificial intelligence for everything, some utterly underestimate the technology.

Machine learning decision systems are getting omnipresent in our lives. From dating apps to rating loan seekers, algorithms affect both our well-being and future. Typically, however, these systems are not infallible. Moreover, complex predictive models are really eager to learn social biases present in historical data that can lead to increasing discrimination. If we want to create models responsibly then we need tools for in-depth validation of models also from the perspective of potential discrimination. This article introduces an R package fairmodels that helps to validate fairness and eliminate bias in classification models in an easy and flexible fashion. The fairmodels package offers a model-agnostic approach to bias detection, visualization and mitigation. The implemented set of functions and fairness metrics enables model fairness validation from different perspectives. The package includes a series of methods for bias mitigation that aim to diminish the discrimination in the model. The package is designed not only to examine a single model, but also to facilitate comparisons between multiple models.

Media bias can lead to increased political polarization, and thus, the need for automatic mitigation methods is growing. Existing mitigation work displays articles from multiple news outlets to provide diverse news coverage, but without neutralizing the bias inherent in each of the displayed articles. Therefore, we propose a new task, a single neutralized article generation out of multiple biased articles, to facilitate more efficient access to balanced and unbiased information. In this paper, we compile a new dataset NeuWS, define an automatic evaluation metric, and provide baselines and multiple analyses to serve as a solid starting point for the proposed task. Lastly, we obtain a human evaluation to demonstrate the alignment between our metric and human judgment.

Every day, militaristic and violent metaphors are used by journalists and political actors alike to communicate and mobilize action. These word choices may seem effective yet, these metaphors, imbued with violent imagery, can be dangerous. From a policy standpoint, they are also ineffective (and potentially harmful). One example is how the global "war on drugs" terminology victimized, stigmatized, and misplaced blame. As noted by others, as with any war, there are always civil rights abuses.

In February, Google announced that it was committing to training 100,000 Black women in digital skills. This announcement arrived as a PR Hail Mary amid the ever-growing industry and academic outcry over Google's firing of prominent, brilliant, respected A.I. researcher Timnit Gebru and recruiter April Christina Curley, both Black women and both exceptional contributors at the company. The backlash occurred during a year of widespread protest against the centuries-old violence of racism and racialized capitalism in the United States. This is not the first time that a prominent tech organization has attempted to "train up" Black Americans. From 1968 to 1972, at least 18 programs to provide computing skills training to Black and brown Americans were established in the United States. They were located in East Coast and California cities, with one in St. Louis, Missouri.

China enlisted surveillance firms to help draw up standards for mass facial recognition systems, researchers said on Tuesday, warning that an unusually heavy emphasis on tracking characteristics such as ethnicity created wide scope for abuse. The technical standards, published by surveillance research group IPVM, specify how data captured by facial recognition cameras across China should be segmented by dozens of characteristics -- from eyebrow size to skin color and ethnicity. "It's the first time we've ever seen public security camera networks that are tracking people by these sensitive categories explicitly at this scale," said the report's author, Charles Rollet. The standards are driving the way surveillance networks are being built across the country -- from residential developments in the capital, Beijing, to police systems in the central province of Hubei, he said. In one instance, the report cites a November 2020 tender for a small "smart" housing project in Beijing, requiring suppliers for its surveillance camera system to meet a standard that allows sorting by skin tone, ethnicity and hairstyle.

We propose a novel interpretable framework for cross-lingual content flagging, which significantly outperforms prior work both in terms of predictive performance and average inference time. The framework is based on a nearest-neighbour architecture and is interpretable by design. Moreover, it can easily adapt to new instances without the need to retrain it from scratch. Unlike prior work, (i) we encode not only the texts, but also the labels in the neighbourhood space (which yields better accuracy), and (ii) we use a bi-encoder instead of a cross-encoder (which saves computation time). Our evaluation results on ten different datasets for abusive language detection in eight languages shows sizable improvements over the state of the art, as well as a speed-up at inference time.

Artificial Intelligence is one of the fastest growing technologies of the 21st century and accompanies us in our daily lives when interacting with technical applications. However, reliance on such technical systems is crucial for their widespread applicability and acceptance. The societal tools to express reliance are usually formalized by lawful regulations, i.e., standards, norms, accreditations, and certificates. Therefore, the T\"UV AUSTRIA Group in cooperation with the Institute for Machine Learning at the Johannes Kepler University Linz, proposes a certification process and an audit catalog for Machine Learning applications. We are convinced that our approach can serve as the foundation for the certification of applications that use Machine Learning and Deep Learning, the techniques that drive the current revolution in Artificial Intelligence. While certain high-risk areas, such as fully autonomous robots in workspaces shared with humans, are still some time away from certification, we aim to cover low-risk applications with our certification procedure. Our holistic approach attempts to analyze Machine Learning applications from multiple perspectives to evaluate and verify the aspects of secure software development, functional requirements, data quality, data protection, and ethics. Inspired by existing work, we introduce four criticality levels to map the criticality of a Machine Learning application regarding the impact of its decisions on people, environment, and organizations. Currently, the audit catalog can be applied to low-risk applications within the scope of supervised learning as commonly encountered in industry. Guided by field experience, scientific developments, and market demands, the audit catalog will be extended and modified accordingly.

Arm said Tuesday that ray tracing and variable rate shading will migrate from the PC to Arm-powered smartphones and tablets as part of Armv9, the next-generation CPU architecture that the company expects will power the next decade of Arm devices. Chips based upon the v9 architecture will be released in 2021, providing an estimated 30-percent improvement in performance over the next two Arm chip generations and the devices that run them. Arm's v9 will also add SVE2, new AI-specific instructions that will probably be used for the AI image processing used on smartphones, such as portrait mode. Arm v9 will also include what Arm is calling Realms, a hardware container of sorts specifically designed to protect virtual machines and secure applications. As an intellectual-property licensing company, Arm enjoys a unique position in the computing industry.