For the last decade or so, most of the'new' legal challenges facing the IT industry have been driven by the Internet, mobile and cloud computing technologies that have altered almost every aspect of society. One of the key areas is in the field of privacy and data security. For larger technology companies, securing and protecting data are key competitive advantages, and privacy and data security are now commonplace on the corporate risk agenda. These issues have created friction with existing laws and regulations, and have highlighted the fact that many laws/regulations have not kept pace with technological development. Further, with the advancement of robotics and artificial intelligence (AI), the questions and issues will become far more complex and challenging.

Like a more crooked version of the Voight-Kampff test from Blade Runner, a new machine learning paper from a pair of Chinese researchers has delved into the controversial task of letting a computer decide on your innocence. Can a computer know if you're a criminal just from your face? In their paper'Automated Inference on Criminality using Face Images', published on the arXiv pre-print server, Xiaolin Wu and Xi Zhang from China's Shanghai Jiao Tong University investigate whether a computer can detect if a human could be a convicted criminal just by analysing his or her facial features. The two say their tests were successful, and that they even found a new law governing "the normality for faces of non-criminals." They described the idea of algorithms that can match and exceed a human's performance in face recognition to infer criminality "irresistible".

One major example of promising ‘smart’ technology in the public sector is the autonomous vehicle (AV). AVs are expected to yield numerous social benefits, such as increasing traffic efficiency, decreasing pollution, and decreasing traffic accidents by 90%. However, a recent 2016 study published by Bonnefon et al. argued that manufacturers and regulators face a major design challenge of balancing competing public preferences: a moral preference for “utilitarian” algorithms; a consumer preference for vehicles that prioritize passenger safety; and a policy preference for minimum government regulation of vehicle algorithm design. Our paper responds to the 2016 study, calling into question the importance of explicitly moral algorithms and the seriousness of the challenge identified by Bonnefon et al. We conclude that the ‘social dilemma’ is probably overstated. Given that attempts to resolve the ‘social dilemma’ are likely to delay the rollout of socially beneficial AVs, we implore the need for further research validating Bonnefon et al.’s conclusions and encourage manufacturers and regulators to commercialize AVs as soon as possible. We discuss the implications of this example for AV’s for the larger context of Cognitive Assistance in other application areas and the government and public policies that are being discussed.

Online “notice and choice” is an essential concept in the US FTC’s Fair Information Practice Principles. Privacy laws based on these principles include requirements for providing notice about data practices and allowing individuals to exercise control over those practices. Internet users need control over privacy, but their options are hidden in long privacy policies which are cumbersome to read and understand. In this paper, we describe several approaches to automatically extract choice instances from privacy policy documents using natural language processing and machine learning techniques. We define a choice instance as a statement in a privacy policy that indicates the user has discretion over the collection, use, sharing, or retention of their data. We describe supervised machine learning approaches for automatically extracting instances containing opt-out hyperlinks and evaluate the proposed methods using the OPP-115 Corpus, a dataset of annotated privacy policies. Extracting information about privacy choices and controls enables the development of concise and usable interfaces to help Internet users better understand the choices offered by online services. The focus of this paper, however, is to describe such methods to automatically extract useful opt-out hyperlinks from privacy policies.

As Hyperloop One continues its attempt at building the future of public transportation, it's moving on without the baggage of a messy lawsuit. The company announced today that it has reached a settlement with former employees, including co-founder and former CTO Brogan BamBrogan. No terms were disclosed, however, the lawsuit contained allegations of financial mismanagement, harassment and threats, which Hyperloop One had responded to with a $250 million suit of its own, claiming the exec had tried to lead a coup within the company. In a memo to current employees, CEO Rob Lloyd looked forward, citing the company's opening of a fabrication facility, acquiring $50 million in financing and new partnerships. Now the company can focus on more standard issues, like delivering on its vision of self-driving vehicles that turn into high-speed train cars.

Algorithms are now used throughout the public and private sectors, informing decisions on everything from education and employment to criminal justice. But despite the potential for efficiency gains, algorithms fed by big data can also amplify structural discrimination, produce errors that deny services to individuals, or even seduce an electorate into a false sense of security. Indeed, there is growing awareness that the public should be wary of the societalrisks posed by over-reliance on these systems and work to hold themaccountable. Various industry efforts, including a consortium of Silicon Valley behemoths, are beginning to grapple with the ethics of deploying algorithms that can have unanticipated effects on society. Algorithm developers and product managers need new ways to think about, design, and implement algorithmic systems in publicly accountable ways. Over the past several months, we and some colleagues have been trying to address these goals by crafting a set of principles for accountable algorithms.

The days of having to remember to your ID with you could soon be a thing of the past. The famous tourist town of Wuzhen, China, is now using face recognition technology to act as its entry pass through the gates of the attraction. The system uses cameras to spot people as they approach the entry, and checks these against a database of registered visitors within a few seconds. The facial recognition technology is thought to be up to 99.77 per cent accurate and able to distinguish people better than a human. Chinese web firm Baidu's system is based on neural networks, which can process huge amounts of data – more than one billion faces – with 99.8 per cent accuracy.

The day before the election, as millions of Americans were feeling confident that the vast majority of the country shared their opinions, a pair of researchers at the University of Southern California Information Sciences Institute published a paper that looked closely at something many of us ignored: the provenance of political tweets. Where do they come from? How many are, in reality, made by humans? And if not, who is designing these crude straw-bots? Analyzing Twitter during three televised debates, they discovered that 20% of all political tweets were made by bots.

Saya had been teaching for seven years. Her impressive but short CV included stints in a few rural areas, overseas and as a substitute teacher. The difference is Saya is a remote controlled robot who taught her first class of 10-year olds in 2009. While we've all heard and read the stories of manual or labour-type jobs easily replaced by robots, increasingly the jobs we previously thought safe are no longer -- teachers, bankers, data analysts and the like are all at risk. But what do we really have to fear?

The House of Lords has passed the Investigatory Powers Bill, putting the huge spying powers on their way to becoming law within weeks. The bill – which forces internet companies to keep records on their users for up to a year, and allows the Government to force companies to hack into or break things they've sold so they can be spied on – has been fought against by privacy campaigners and technology companies including Apple and Twitter. But the Government has worked to continue to pass the bill, despite objections from those companies that the legislation is not possible to enforce and would make customers unsafe. In its facilities, JAXA develop satellites and analyse their observation data, train astronauts for utilization in the Japanese Experiment Module'Kibo' of the International Space Station (ISS) and develop launch vehicles 23/40 The robot developed by Seed Solutions sings and dances to the music during the Japan Robot Week 2016 at Tokyo Big Sight. At this biennial event, the participating companies exhibit their latest service robotic technologies and components 24/40 The robot developed by Seed Solutions sings and dances to music during the Japan Robot Week 2016 at Tokyo Big Sight 25/40 Government and industry are working together on a robot-like autopilot system that could eliminate the need for a second human pilot in the cockpit 26/40 Aurora Flight Sciences' technicians work on an Aircrew Labor In-Cockpit Automantion System (ALIAS) device in the firm's Centaur aircraft at Manassas Airport in Manassas, Va.