Deloitte released a report which highlights how trends in Technology, Media & Telecommunications (TMT) may affect businesses and consumers worldwide in 2022. The report underscores how many of these trends are being driven by the global pandemic's economic and societal shifts, resulting in an increasingly connected and multi-device world, fueling the world's need for more chips, growth in connectivity, and entertainment options. "The pandemic increased the need to maintain connections, improve productivity and experience entertainment, with accelerated adoption from both consumers and businesses alike," said Kevin Westcott, vice chair, Deloitte, U.S. TMT and global Telecommunications, Media and Entertainment (TME) practice leader. "In 2022, we foresee these behaviors continuing to grow, but amid a backdrop of challenges. Supply chain woes, increasing regulatory issues and changing media habits will be at the forefront of business leaders' minds as these challenges impact their ability to meet market demands." Many types of chips will still be in short supply during 2022, but it will be less severe than it was for most of 2021, and it will not affect all chips.

Clearview AI is on track to receive a US patent for its facial recognition technology, according to a report from Politico. The company was reportedly sent a "notice of allowance" by the US Patent and Trademark Office, which means that once it pays the required administration fees, its patent will be officially approved. Clearview AI builds its facial recognition database using images of people that it scrapes across social media (and the internet in general), a practice that has the company steeped in controversy. The company's patent application details its use of a "web crawler" to acquire images, even noting that "online photos associated with a person's account may help to create additional records of facial recognition data points," which its machine learning algorithm can then use to find and identify matches. Critics argue that Clearview AI's facial recognition technology is a violation of privacy and that it may negatively impact minority communities.

"We need to know how the many subjective decisions that go into building a model lead to the observed results, and why those decisions were thought justified at the time, just to have a chance at disentangling everything when something goes wrong," the paper reads. "Algorithmic impact assessments cannot solve all algorithmic harms, but they can put the field and regulators in better positions to avoid the harms in the first place and to act on them once we know more." A revamped version of the Algorithmic Accountability Act, first introduced in 2019, is now being discussed in Congress. According to a draft version of the legislation reviewed by WIRED, the bill would require businesses that use automated decision-making systems in areas such as health care, housing, employment, or education to carry out impact assessments and regularly report results to the FTC. A spokesperson for Senator Ron Wyden (D-Ore.), a cosponsor of the bill, says it calls on the FTC to create a public repository of automated decision-making systems and aims to establish an assessment process to enable future regulation by Congress or agencies like the FTC.

A politically informed citizenry is imperative for a welldeveloped democracy. While the US government has pursued policies for open data, these efforts have been insufficient in achieving an open government because only people with technical and domain knowledge can access information in the data. In this work, we conduct user interviews to identify wants and needs among stakeholders. We further use this information to sketch out the foundational requirements for a functional political information technical system.

Obtaining a well-trained model involves expensive data collection and training procedures, therefore the model is a valuable intellectual property. Recent studies revealed that adversaries can `steal' deployed models even when they have no training samples and can not get access to the model parameters or structures. Currently, there were some defense methods to alleviate this threat, mostly by increasing the cost of model stealing. In this paper, we explore the defense from another angle by verifying whether a suspicious model contains the knowledge of defender-specified \emph{external features}. Specifically, we embed the external features by tempering a few training samples with style transfer. We then train a meta-classifier to determine whether a model is stolen from the victim. This approach is inspired by the understanding that the stolen models should contain the knowledge of features learned by the victim model. We examine our method on both CIFAR-10 and ImageNet datasets. Experimental results demonstrate that our method is effective in detecting different types of model stealing simultaneously, even if the stolen model is obtained via a multi-stage stealing process. The codes for reproducing main results are available at Github (https://github.com/zlh-thu/StealingVerification).

Artificial intelligence is being utilized in many domains as of late, and the legal system is no exception. However, as it stands now, the number of well-annotated datasets pertaining to legal documents from the Supreme Court of the United States (SCOTUS) is very limited for public use. Even though the Supreme Court rulings are public domain knowledge, trying to do meaningful work with them becomes a much greater task due to the need to manually gather and process that data from scratch each time. Hence, our goal is to create a high-quality dataset of SCOTUS court cases so that they may be readily used in natural language processing (NLP) research and other data-driven applications. Additionally, recent advances in NLP provide us with the tools to build predictive models that can be used to reveal patterns that influence court decisions. By using advanced NLP algorithms to analyze previous court cases, the trained models are able to predict and classify a court's judgment given the case's facts from the plaintiff and the defendant in textual format; in other words, the model is emulating a human jury by generating a final verdict.

Civil rights groups argue that facial recognition technology is error-prone, misidentifying women and minorities at higher rates than it does white men and sometimes leading to false arrests. Clearview AI has gotten the green light on a federal patent for its facial recognition technology -- an award that the company says is the first to cover a so-called "search engine for faces" that crawls the internet to find matches. Clearview's software -- which scrapes public images from social media to help law enforcement match images in government databases or surveillance footage -- has long faced fire from privacy advocates who say it uses people's faces without their knowledge or consent. Civil rights groups also argue that facial recognition technology is generally error-prone, misidentifying women and minorities at higher rates than it does white men and sometimes leading to false arrests.

And of course, that's because they're not actually written for you, or any of the other billions of people who click to agree to their inscrutable legalese. Instead, like bad poetry and teenagers' diaries, those millions upon millions of words are produced for the benefit of their authors, not readers--the lawyers who wrote those get-out clauses to protect their Silicon Valley employers. But one group of academics has proposed a way to make those virtually illegible privacy policies into the actual tool of consumer protection they pretend to be: an artificial intelligence that's fluent in fine print. Today, researchers at Switzerland's Federal Institute of Technology at Lausanne (EPFL), the University of Wisconsin and the University of Michigan announced the release of Polisis--short for "privacy policy analysis"--a new website and browser extension that uses their machine-learning-trained app to automatically read and make sense of any online service's privacy policy, so you don't have to. In about 30 seconds, Polisis can read a privacy policy it's never seen before and extract a readable summary, displayed in a graphic flow chart, of what kind of data a service collects, where that data could be sent, and whether a user can opt out of that collection or sharing.

The Internet generates large volumes of data at a high rate, in particular, posts on social networks. Although social network data has numerous semantic adulterations, and is not intended to be a source of geo-spatial information, in the text of posts we find pieces of important information about how people relate to their environment, which can be used to identify interesting aspects of how human beings interact with portions of land based on their activities. This research proposes a methodology for the identification of land uses using Natural Language Processing (NLP) from the contents of the popular social network Twitter. It will be approached by identifying keywords with linguistic patterns from the text, and the geographical coordinates associated with the publication. Context-specific innovations are introduced to deal with data across South America and, in particular, in the city of Arequipa, Peru. The objective is to identify the five main land uses: residential, commercial, institutional-governmental, industrial-offices and unbuilt land. Within the framework of urban planning and sustainable urban management, the methodology contributes to the optimization of the identification techniques applied for the updating of land use cadastres, since the results achieved an accuracy of about 90%, which motivates its application in the real context. In addition, it would allow the identification of land use categories at a more detailed level, in situations such as a complex/mixed distribution building based on the amount of data collected. Finally, the methodology makes land use information available in a more up-to-date fashion and, above all, avoids the high economic cost of the non-automatic production of land use maps for cities, mostly in developing countries.

Clearview AI is about to get formal acknowledgment for its controversial facial recognition technology. Politico reports Clearview has received a US Patent and Trademark Office "notice of allowance" indicating officials will approve a filing for its system, which scans faces across public internet data to find people from government lists and security camera footage. The company just has to pay administrative fees to secure the patent. In a Politico interview, Clearview founder Hoan Ton-That claimed this was the first facial recognition patent involving "large-scale internet data." The firm sells its tool to government clients (including law enforcement) hoping to accelerate searches.