This photo taken on February 5, 2018 shows a police officer wearing a pair of smartglasses with a facial recognition system at Zhengzhou East Railway Station in Zhengzhou in China's central Henan province. Chinese police are sporting high-tech sunglasses that can spot suspects in a crowded train station, the newest use of facial recognition that has drawn concerns among human rights groups. We seem to be heading into a future where facial recognition technologies are going to be part of everyday life. Cities all over the world are now bristling with cameras, and in the case of China it is impossible to avoid being monitored either by CCTV or even by police wearing special glasses and then logged onto a database that checks on your habits, your social credit and even who your friends are. At the same time, cameras and facial recognition are increasingly being used in public and private buildings.

Black box systems for automated decision making, often based on machine learning over (big) data, map a user's features into a class or a score without exposing the reasons why. This is problematic not only for lack of transparency, but also for possible biases hidden in the algorithms, due to human prejudices and collection artifacts hidden in the training data, which may lead to unfair or wrong decisions. We introduce the local-to-global framework for black box explanation, a novel approach with promising early results, which paves the road for a wide spectrum of future developments along three dimensions: (i) the language for expressing explanations in terms of highly expressive logic-based rules, with a statistical and causal interpretation; (ii) the inference of local explanations aimed at revealing the logic of the decision adopted for a specific instance by querying and auditing the black box in the vicinity of the target instance; (iii), the bottom-up generalization of the many local explanations into simple global ones, with algorithms that optimize the quality and comprehensibility of explanations.

Combinatorial preference aggregation has many applications in AI. Given the exponential nature of these preferences, compact representations are needed and ($m$)CP-nets are among the most studied ones. Sequential and global voting are two ways to aggregate preferences over CP-nets. In the former, preferences are aggregated feature-by-feature. Hence, when preferences have specific feature dependencies, sequential voting may exhibit voting paradoxes, i.e., it might select sub-optimal outcomes. To avoid paradoxes in sequential voting, one has often assumed the $\mathcal{O}$-legality restriction, which imposes a shared topological order among all the CP-nets. On the contrary, in global voting, CP-nets are considered as a whole during preference aggregation. For this reason, global voting is immune from paradoxes, and there is no need to impose restrictions over the CP-nets' topological structure. Sequential voting over $\mathcal{O}$-legal CP-nets has extensively been investigated. On the other hand, global voting over non-$\mathcal{O}$-legal CP-nets has not carefully been analyzed, despite it was stated in the literature that a theoretical comparison between global and sequential voting was promising and a precise complexity analysis for global voting has been asked for multiple times. In quite few works, very partial results on the complexity of global voting over CP-nets have been given. We start to fill this gap by carrying out a thorough complexity analysis of Pareto and majority global voting over not necessarily $\mathcal{O}$-legal acyclic binary polynomially connected (m)CP-nets. We settle these problems in the polynomial hierarchy, and some of them in PTIME or LOGSPACE, whereas EXPTIME was the previously known upper bound for most of them. We show various tight lower bounds and matching upper bounds for problems that up to date did not have any explicit non-obvious lower bound.

An image from the product page of Amazon's Rekognition service, which provides image and video facial and item recognition and analysis. SAN FRANCISCO -- The city of Orlando's police department has ended its test of a facial recognition program created by Amazon that has come under fire from privacy advocates. But other law enforcement organizations say they continue to use it to solve crimes. Amazon's Rekognition software works by comparing images provided by the customer to a database of images the customer has also provided. It searches for a match using the computing power of Amazon's cloud computing network AWS.

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Last week, the United States Patent and Trademark Office (USPTO) hosted the annual meeting of the heads of the world's five largest intellectual property offices, commonly referred to as the IP5 (see a related press release). In addition to the USPTO, the members of the IP5 include the European Patent Office (EPO), the Japan Patent Office (JPO), the Korean Intellectual Property Office (KIPO), and the State Intellectual Property Office of the People's Republic of China (SIPO). Together, the five offices handle approximately 80 percent of the world's patent applications. The goal of the IP5 is to eliminate unnecessary duplication of efforts amongst the IP5 offices as well as increasing the efficiency of patent examination efficiency and quality. During the meeting, the impact of artificial intelligence (AI) on the patent system was identified as one of the main strategic priorities for the offices.

The use of artificial intelligence (AI) will truly revolutionize compliance, allowing it to more efficiently and robustly be operationalized into the fabric of an organization employing this new technology. Not only is this one exciting reason to be involved with the compliance profession today, tomorrow, and into the future, it also make companies run more efficiently and with higher profitability. One area with the prospect of greater application is machine learning of bribery and corruption schemes. This is the type of AI that moves past the information that compliance professionals may be able to pull from corporate databases, such as gifts, travel, and entertainment (GTE). Consider when one employee spends a large amount of GTE not on one foreign government official but on one department.

Dear EarthTalk: What are some ways Artificial Intelligence (AI) is being used to fight climate change and otherwise protect the environment? Artificial Intelligence (AI), defined as the capability of machines to imitate intelligent human behavior and learn from data, is considered by many to be the final frontier of computing. And environmentalists and tech companies are now harnessing the power of AI to service to the environment. To wit, Microsoft announced in December 2017 that it is expanding its "AI for Earth" program and committing $50 million over the next five years to put AI technologies in the hands of individuals and organizations working to solve global environmental challenges, including climate change as well as water, agriculture and biodiversity issues. Lucas Joppa, Microsoft's first Chief Environmental Scientist, is convinced that AI is now mature enough and the global environmental crisis acute enough to justify the creation of an AI platform for the planet.

Case study: Some car makers, including Volvo, Google, and Mercedes, have already said they would accept full liability for their vehicles' actions when they are in autonomous mode. Even without such a pledge, it's likely that manufacturers would end up paying if their autonomous car caused harm. If the offending car were considered a defective product, its maker could be held liable under strict product-design standards, potentially leading to class-action lawsuits and expensive product recalls -- like Takata faced for its dangerous airbags. Another possibility: Going deeper into the system, the AI itself could be held responsible, according to Gabriel Hallevy, a law professor at Ono Academic College in Israel, who wrote a book about AI and criminal negligence. That still means its programmer or manufacturer could be found negligent as well, or even accomplice to a crime.

Instead of merely selling copies of 1984, Amazon appears determined to help bring the dystopian classic's vision of widespread government surveillance to life. And Amazon employees are really not happy about it. In 2016, Amazon unveiled Rekognition, an AI-powered facial recognition software that scans videos or photos to detect people or objects. It can analyze a person's face to determine their emotions, identify 100 faces in a single photo, and track a person throughout a video even if they leave and reenter the field of view. In other words, it's a powerful surveillance tool, and government agencies and law enforcement are apparently two of Amazon's target customers.