To learn how to behave, the current revolutionary generation of AIs must be trained on vast quantities of published images, written works, and sounds, many of which fall within the core subject matter of copyright law. To some, the use of copyrighted works as training sets for AI is merely a transitory and non-consumptive use that does not materially interfere with owners' content or copyrights protecting it. Companies that use such content to train their AI engine often believe such usage should be considered "fair use" under United States law (sometimes known as "fair dealing" in other countries). By contrast, many copyright owners, as well as their supporters, consider the incorporation of copyrighted works into training sets for AI to constitute misappropriation of owners' intellectual property, and, thus, decidedly not fair use under the law. This debate is vital to the future trajectory of AI and its applications. In this article, we analyze the arguments in favor of, and against, viewing the use of copyrighted works in training sets for AI as fair use. We call this form of fair use "fair training". We identify both strong and spurious arguments on both sides of this debate. In addition, we attempt to take a broader perspective, weighing the societal costs (e.g., replacement of certain forms of human employment) and benefits (e.g., the possibility of novel AI-based approaches to global issues such as environmental disruption) of allowing AI to make easy use of copyrighted works as training sets to facilitate the development, improvement, adoption, and diffusion of AI. Finally, we suggest that the debate over AI and copyrighted works may be a tempest in a teapot when placed in the wider context of massive societal challenges such as poverty, equality, climate change, and loss of biodiversity, to which AI may be part of the solution.

Data sharing remains a major hindering factor when it comes to adopting emerging AI technologies in general, but particularly in the agri-food sector. Protectiveness of data is natural in this setting; data is a precious commodity for data owners, which if used properly can provide them with useful insights on operations and processes leading to a competitive advantage. Unfortunately, novel AI technologies often require large amounts of training data in order to perform well, something that in many scenarios is unrealistic. However, recent machine learning advances, e.g. federated learning and privacy-preserving technologies, can offer a solution to this issue via providing the infrastructure and underpinning technologies needed to use data from various sources to train models without ever sharing the raw data themselves. In this paper, we propose a technical solution based on federated learning that uses decentralized data, (i.e. data that are not exchanged or shared but remain with the owners) to develop a cross-silo machine learning model that facilitates data sharing across supply chains. We focus our data sharing proposition on improving production optimization through soybean yield prediction, and provide potential use-cases that such methods can assist in other problem settings. Our results demonstrate that our approach not only performs better than each of the models trained on an individual data source, but also that data sharing in the agri-food sector can be enabled via alternatives to data exchange, whilst also helping to adopt emerging machine learning technologies to boost productivity.

We provide a psychometric-grounded exposition of bias and fairness as applied to a typical machine learning pipeline for affective computing. We expand on an interpersonal communication framework to elucidate how to identify sources of bias that may arise in the process of inferring human emotions and other psychological constructs from observed behavior. Various methods and metrics for measuring fairness and bias are discussed along with pertinent implications within the United States legal context. We illustrate how to measure some types of bias and fairness in a case study involving automatic personality and hireability inference from multimodal data collected in video interviews for mock job applications. We encourage affective computing researchers and practitioners to encapsulate bias and fairness in their research processes and products and to consider their role, agency, and responsibility in promoting equitable and just systems. Personal use of this material is permitted. The tools used in affective computing (AC), which enable machines to identify people's behaviors and mental states, are being increasingly utilized in education, healthcare, and the workplace. One application is to aid in the allocation of limited resources (e.g., counseling, mental health care, in-person interviews) via automated screening [1-3]. In these types of high-stakes scenarios, the assessments provided by AC systems can directly affect the decision processes which influence the amount of attention, care, and opportunities afforded to individuals. As such, it is important that these processes are accurate, unbiased, and fair because any deficiencies or errors present in these systems stemming from the data they were trained on, the types of algorithms used, or the decision processes themselves, may disproportionately impact different groups of people and lead to ethical and legal concerns, not to mention pain and suffering for the vulnerable groups impacted. Simply put, AC systems must deter, not propagate, extant systems of inequity and injustice. Fortunately, we have decades of guidance on how to construct fair and unbiased measurement systems.

We describe how interpretable boosting algorithms based on ridge-regularized generalized linear models can be used to analyze high-dimensional environmental data. We illustrate this by using environmental, social, human and biophysical data to predict the financial vulnerability of farmers in Chile and Tunisia against climate hazards. We show how group structures can be considered and how interactions can be found in high-dimensional datasets using a novel 2-step boosting approach. The advantages and efficacy of the proposed method are shown and discussed. Results indicate that the presence of interaction effects only improves predictive power when included in two-step boosting. The most important variable in predicting all types of vulnerabilities are natural assets. Other important variables are the type of irrigation, economic assets and the presence of crop damage of near farms.

Video appeared to show a drone being shot down over the Kremlin Wednesday, in what Russia says was an assassination attempt against President Vladimir Putin. The State Department says that it's "unable to confirm" the authenticity of a video purporting to show a drone attack on the Kremlin. State Department Principal Deputy Spokesperson Vedant Patel was responding to claims from Russian government officials who said that Ukrainian forces attempted to kill President Vladimir Putin by a failed drone attack. "We are aware of these, but unable to confirm the authenticity of this," Patel said during a press conference Wednesday. "We're continuing to assess this and confirm the authenticity."

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The U.S.-led coalition carried out a drone strike Wednesday in northwestern Syria targeting a senior al-Qaida leader, the U.S. military said. The Syrian Observatory for Human Rights, an opposition war monitor, said the strike hit a chicken farm near the town of Harem, killing one person. It said the dead man has not been identified yet.

Russia has threatened to retaliate against Ukraine for a failed attempt to assassinate President Vladimir Putin in an alleged drone attack on the Kremlin citadel in Moscow. Kyiv has denied any involvement and accused Russia of readying itself for a major offensive in Ukraine. Here's what we know so far about Wednesday's alleged attack: Moscow said two drones had been used in the alleged attack on Putin's residence in the Kremlin citadel, but had been disabled by electronic defences. "We regard these actions as a planned terrorist act and an attempt on the president's life, carried out on the eve of Victory Day, the May 9 Parade, at which the presence of foreign guests is also planned," the Kremlin said in a statement. "The Russian side reserves the right to take retaliatory measures where and when it sees fit."

The Republican National Committee fired off an attack ad as soon as President Joe Biden announced his reelection campaign last week. The 30-second spot which used fake visuals of China invading Taiwan, financial markets crashing and immigrants overrunning the border sported a disclaimer: "Built entirely with AI imagery." The ad – which the GOP called "an AI-generated look into the country's possible future if Joe Biden is re-elected in 2024" – is a sign of what's to come in the 2024 presidential election, experts say. AI crack down?Senate leader Schumer unveils plans to crack down on AI Fake Twitter accountsIs that Twitter account real? 4 ways to help you spot a fake account. Even as the technology grows more sophisticated and powerful, spreading into all aspects of American life, there are still very few rules governing its use.

Fox News correspondent Grady Trimble has the latest on fears the technology will spiral out of control on'Special Report.' Tech experts warned that premature regulation of artificial intelligence could give China a leg up, allowing the country to meet its goals of dominating the world in technology. "The United States is in a relatively precarious position, and we have to make sure we move fastest on the technology," Alexandr Wang, the founder and CEO of Scale AI said at the Milken Institute Global Conference Monday. China has released plans to make the country the global leader in AI by 2030, as well as a National Innovation-Driven Development Strategy for use by the country's military. Panelists speak about artificial intelligence at the Milken Institute Global Conference.

Fox News' Alex Hogan reports on Russia claiming Ukraine attacked the Kremlin in an attempt to assassinate Vladmir Putin as the war in Ukraine rages on. Russia has trimmed down annual Victory Day celebrations, with some claiming the Kremlin fears protests and dissent following continued and severe losses in Ukraine. Russian President Vladimir Putin has used the celebrations, which mark the Soviet Union's triumph over Nazi Germany in World War II, as propaganda opportunities. He used 2021 to warn that Russia's enemies once more followed "much of the ideology of the Nazis," a rallying cry he repeated throughout his invasion of Ukraine, and in 2022 he marched in the Immortal Regiment procession while holding a picture of his father in military attire. However, this year's celebrations will have much less fanfare as governors in Belgorod, Kursk, Voronezh, Oryol and Pskov as well as the Crimean Peninsula have all canceled their parades, The Guardian reported.