Very little is known about the New York Police Department's use of facial recognition technology. What kind of facial recognition is in place? Which databases are being used? How many people have been scanned, and why? How long is their data retained?

Ocado and Google DeepMind executives are among a cohort of experts that have been called to advise the Government on how to boost the use of artificial intelligence in Britain. Paul Clarke, chief technology officer of the e-commerce company, and DeepMind co-founder Mustafa Suleyman will join the new lineup of the Government's AI council, an advisory group set up as part of a push to boost investment in the technology. Mastercard vice chairman Ann Cairns, Amazon machine learning director Neil Lawrence and Microsoft research lab director Chris Bishop are also among those who gained seats on the new council. The executives are expected to promote the use of AI by businesses in the UK and advise the Government about future public investments in the industry. The Government already set aside £3m for AI projects aimed at boosting productivity in financial and legal services last year as part of this effort.

Police departments across the nation are generating leads and making arrests by feeding celebrity photos, CGI renderings, and manipulated images into facial recognition software. Often unbeknownst to the public, law enforcement is identifying suspects based on "all manner of'probe photos,' photos of unknown individuals submitted for search against a police or driver license database," a study published on Thursday by the Georgetown Law Center on Privacy and Technology reported. The new research comes on the heels of a landmark privacy vote on Tuesday in San Francisco, which is now the first US city to ban the use of facial recognition technology by police and government agencies. A recent groundswell of opposition has led to the passage of legislation that aims to protect marginalized communities from spy technology. These systems "threaten to fundamentally change the nature of our public spaces," said Clare Garvie, author of the study and senior associate at the Georgetown Law Center on Privacy and Technology.

Here's what you need to know in business news. The city's Board of Supervisors voted on Tuesday to prohibit the use of facial recognition technology within city limits. It's a somewhat symbolic move: The police there don't currently use the stuff, and the places where it is in use -- seaports and airports -- are under federal jurisdiction and therefore unaffected by the new regulation. The major television networks tried to sell their fall advertising slots in an annual pageant known as the upfronts. In a week of star-studded presentations, skits and boozy mingling, representatives of major advertisers flocked to New York to see what the networks have in store.

As lawmakers grapple with how to shape legislation dealing with artificial intelligence, the clerk of the House is developing an AI tool to automate the process of analyzing differences between bills, amendments and current laws. That's according to Robert F. Reeves, the deputy clerk of the House, who on Friday told the Select Committee on the Modernization of Congress that his office is working on an "artificial intelligence engine" that may be ready as soon as next year. The idea, Reeves said, is to offer members and staff a tool that would accurately compare legislative text. He said it's already available to Office of Legislative Counsel staffers, who then must check the accuracy with human intelligence. It's about 90 percent there, he told the panel.

The Diet on Friday enacted legislative revisions aimed at creating systems to ensure the safety of self-driving vehicles. The revisions to the Road Transport Vehicle Act, approved unanimously by the House of Councilors at a plenary session, call for the applying of vehicle safety standards to self-driving equipment necessary to check the surroundings, including cameras and radars. Under the revised law, special certification will be granted to auto safety inspection business operators capable of undertaking maintenance work for self-driving equipment. The original law did not have provisions that assumed vehicles would ever be self-driving. The revisions also require automakers to provide technical information necessary to carry out inspections of self-driving equipment.

San Francisco is on track to become the first U.S. city to ban the use of facial recognition by police and other city agencies, reflecting a growing backlash against a technology that's creeping into airports, motor vehicle departments, stores, stadiums and home security cameras. Government agencies around the U.S. have used the technology for more than a decade to scan databases for suspects and prevent identity fraud. But recent advances in artificial intelligence have created more sophisticated computer vision tools, making it easier for police to pinpoint a missing child or protester in a moving crowd or for retailers to analyze a shopper's facial expressions as they peruse store shelves. Efforts to restrict its use are getting pushback from law enforcement groups and the tech industry, though it's far from a united front. Microsoft, while opposed to an outright ban, has urged lawmakers to set limits on the technology, warning that leaving it unchecked could enable an oppressive dystopia reminiscent of George Orwell's novel "1984."

Artificial intelligence (AI) algorithms are generally hungry for data, a trend which is accelerating. A new breed of AI approaches, called lifelong learning machines, are being designed to pull data continually and indefinitely. But this is already happening with other AI approaches, albeit with human intervention. A steady stream of data is the fuel for coveted results. But, with the ever-increasing importance of data, the stakes of data bias are growing ever higher.

Law firms and legal departments are not taking advantage of artificial intelligence and machine learning tools that could be helping them run more efficiently, according to a new Bloomberg Law survey. Not On Board: Some 54 percent of respondents to the survey said they don't use these tools, while only 23 percent did. Another 24 percent were unsure about whether they used AI and machine learning, which Molly Huie, Bloomberg Law's team leader for data analysis and surveys, said could...

This paper proposes a theoretical analysis of one approach to the eXplainable AI (XAI) problem, using post-hoc explanation-by-example, that relies on the twinning of artificial neural networks (ANNs) with case-based reasoning (CBR) systems; so-called ANN-CBR twins. It surveys these systems to advance a new theoretical interpretation of previous work and define a road map for CBR's further role in XAI. A systematic survey of 1102 papers was conducted to identify a fragmented literature on this topic and trace its influence to more recent work involving deep neural networks (DNNs). The twin-system approach is advanced as one possible coherent, generic solution to the XAI problem. The paper concludes by road-mapping future directions for this XAI solution, considering (i) further tests of feature-weighting techniques, (ii) how explanatory cases might be deployed (e.g., in counterfactuals, a fortori cases), and (iii) the unwelcome, much-ignored issue of user evaluation.