The costs and impacts of government corruption range from impairing a country's economic growth to affecting its citizens' well-being and safety. Public contracting between government dependencies and private sector instances, referred to as public procurement, is a fertile land of opportunity for corrupt practices, generating substantial monetary losses worldwide. Thus, identifying and deterring corrupt activities between the government and the private sector is paramount. However, due to several factors, corruption in public procurement is challenging to identify and track, leading to corrupt practices going unnoticed. This paper proposes a machine learning model based on an ensemble of random forest classifiers, which we call hyper-forest, to identify and predict corrupt contracts in M\'exico's public procurement data. This method's results correctly detect most of the corrupt and non-corrupt contracts evaluated in the dataset. Furthermore, we found that the most critical predictors considered in the model are those related to the relationship between buyers and suppliers rather than those related to features of individual contracts. Also, the method proposed here is general enough to be trained with data from other countries. Overall, our work presents a tool that can help in the decision-making process to identify, predict and analyze corruption in public procurement contracts.

Electronic Health Records (EHRs) are a valuable asset to facilitate clinical research and point of care applications; however, many challenges such as data privacy concerns impede its optimal utilization. Deep generative models, particularly, Generative Adversarial Networks (GANs) show great promise in generating synthetic EHR data by learning underlying data distributions while achieving excellent performance and addressing these challenges. This work aims to review the major developments in various applications of GANs for EHRs and provides an overview of the proposed methodologies. For this purpose, we combine perspectives from healthcare applications and machine learning techniques in terms of source datasets and the fidelity and privacy evaluation of the generated synthetic datasets. We also compile a list of the metrics and datasets used by the reviewed works, which can be utilized as benchmarks for future research in the field. We conclude by discussing challenges in GANs for EHRs development and proposing recommended practices. We hope that this work motivates novel research development directions in the intersection of healthcare and machine learning.

Consider the relationship between a regulator (the principal) and a pharmaceutical company (the agent). The pharmaceutical company wishes to sell a product to make a profit, and the FDA wishes to ensure that only efficacious drugs are released to the public. The efficacy of the drug is not known to the FDA, so the pharmaceutical company must run a costly trial to prove efficacy to the FDA. Critically, the statistical protocol used to establish efficacy affects the behavior of a strategic, self-interested pharmaceutical company; a lower standard of statistical evidence incentivizes the pharmaceutical company to run more trials for drugs that are less likely to be effective, since the drug may pass the trial by chance, resulting in large profits. The interaction between the statistical protocol and the incentives of the pharmaceutical company is crucial to understanding this system and designing protocols with high social utility. In this work, we discuss how the principal and agent can enter into a contract with payoffs based on statistical evidence. When there is stronger evidence for the quality of the product, the principal allows the agent to make a larger profit. We show how to design contracts that are robust to an agent's strategic actions, and derive the optimal contract in the presence of strategic behavior.

The use of big data and algorithms to automate decision-making has been on the rise for many years. Data collection and "commercial surveillance" is a pervasive practice among social media companies and may other providers of online services. Join us as we consider the Federal Trade Commission's proposed rulemaking considering these issues as well as the "Blueprint for an AI Bill of Rights – Making Automated Systems Work for the American People" recently released by the White House Office of Science and Technology. CLE credit: CLE credit in CA, FL, IL, NJ (via reciprocity), NY, PA, TX, and VA is currently pending approval.

The U.S. Food and Drug Administration (FDA), Health Canada, and the United Kingdom's Medicines and Healthcare products Regulatory Agency (MHRA) have jointly identified 10 guiding principles that can inform the development of Good Machine Learning Practice (GMLP). These guiding principles will help promote safe, effective, and high-quality medical devices that use artificial intelligence and machine learning (AI/ML). Artificial intelligence and machine learning technologies have the potential to transform health care by deriving new and important insights from the vast amount of data generated during the delivery of health care every day. They use software algorithms to learn from real-world use and in some situations may use this information to improve the product's performance. But they also present unique considerations due to their complexity and the iterative and data-driven nature of their development.

China's Cyberspace Administration recently issued regulations prohibiting the creation of AI-generated media without clear labels, such as watermarks--among other policies--reports The Register. The new rules come as part of China's evolving response to the generative AI trend that has swept the tech world in 2022, and they will take effect on January 10, 2023. In China, the Cyberspace Administration oversees the regulation, oversight, and censorship of the Internet. Under the new regulations, the administration will keep a closer eye on what it calls "deep synthesis" technology. In a news post on the website of China's Office of the Central Cyberspace Affairs Commission, the government outlined its reasons for issuing the regulation.

The US department for energy has announced that it has made a'major scientific breakthrough' in the race to recreate nuclear fusion. At a press conference on Tuesday US energy secretary, Jennifer Granholm, said scientists at the Lawrence Livermore National Laboratory in California'achieved fusion ignition', which is'creating more energy from fusion reactions than the energy used to start the process.' Describing the experiments results as a'BFD' [Big Fucking Deal], she added that'this milestone moves us one significant step closer to the possibility of zero carbon abundant fusion energy powering our society'

When Melissa Heikkilä, our senior AI reporter, tried the new viral AI avatar app Lensa, she was hoping to get results similar to other colleagues at MIT Technology Review, who got realistic yet flattering avatars--think astronauts, and fierce warriors. Instead, she got tons of nudes. Out of the generated 100 avatars, 16 were topless, while another 14 depicted her in extremely skimpy clothes and overtly sexualized poses. Many of the avatars were of generic Asian women clearly modeled on anime or video-game characters, or, most likely, porn. Another colleague with Chinese heritage got similar results: reams and reams of pornified avatars. Its results are generated using Stable Diffusion, an AI model that draws from a massive open-source data set compiled by scraping images from the internet.

Grant money meant to help cities prepare for terror attacks is being spent on "massive purchases of surveillance technology" for US police departments, a new report by the advocacy organizations Action Center on Race and Economy (ACRE), LittleSis, MediaJustice, and the Immigrant Defense Project shows. Shopping for AI-powered spytech: For example, the Los Angeles Police Department used funding intended for counterterrorism to buy automated license plate readers worth at least $1.27 million, radio equipment worth upwards of $24 million, Palantir data fusion platforms (often used for AI-powered predictive policing), and social media surveillance software. Why this matters: For various reasons, a lot of problematic tech ends up in high-stake sectors such as policing with little to no oversight. For example, the facial recognition company Clearview AI offers "free trials" of its tech to police departments, which allows them to use it without a purchasing agreement or budget approval. Federal grants for counterterrorism don't require as much public transparency and oversight.

December 13, 2022 – videantis GmbH, provider of a unified platform for combined processing of algorithms as diverse as AI (Artificial Intelligence), multi-modal sensor data processing and fusion, or video coding on a single architecture, today announced the tape-out of the FlexAISIC AI chip based on its flexible v-MP6000UDX unified processing platform. The tape-out has been achieved in collaboration with the Fraunhofer Institute for Integrated Circuits IIS, and the chip development is funded by the German Federal Ministry of Education and Research (BMBF) within the KI-FLEX project. KI-FLEX is part of a broader initiative driven by the German BMBF to push research of AI-based technologies for autonomous driving. KI-FLEX develops a powerful and highly energy-efficient hardware platform and the associated software framework for AI-based processing and merging of data from different sensors, resulting in fast and reliable perception and localization for autonomous driving. The videantis v-MP6000UDX platform as the core component of the FlexAISIC is a highly scalable multi-core architecture combined with a tailored bus fabric and multi-banked shared on-chip SRAM which delivers extreme efficiency and performance for a variety of algorithm types like deep learning, computer vision, signal processing, and video coding.