The legal media has lately had a mania for tech headlines. Many commentators claim that tech, especially artificial intelligence (AI), will do something to Big Law. Tech more likely will do something in it: incremental change. I start with the case against disruption, then look at four headline-grabbing technologies: AI, Bots, Big Data, and Blockchain. By the late 1980s, a few law firms had most of their lawyers using PCs.

This month US Customs and Border Protection (CBP) posted the latest in a series of Privacy Impact Assessments (PIAs) for its Traveler Verification Service (TVS) program. The latest PIA gives notice (although not in the form required by Federal law) that CBP and its airline and airport partners are carrying out a second expanded phase of "demonstrations" of TVS, an identity-as-a-service scheme designed to use automated recognition of images from a shared CBP/airline/airport database of facial photos for purposes including surveillance and control (for CBP) and business process automation and price personalization (for airlines and airports). CBP (1) describes TVS as a "biometric exit" program, (2) describes the current use of TVS as merely a "demonstration", (3) continues to claim that airlines and airports "have no interest in keeping or retaining" facial images any longer, or using them for any other purposes, than is required by CBP for "security", and (4) says that U.S citizens aren't required to submit to mug shots. These claims are intended to lull the public into not protesting: "This is only a test, using photos for limited purposes. The photos will be deleted once you get on the plane, and not used for nay commercial or other purpose."

The Justice Ministry will impose new regulations on Japanese-language schools in October to ensure students who enter Japan to learn the language do not spend the majority of their stay working instead of studying. The change was implemented after one applicant raised the ministry's eyebrows by asking about setting up a school that would be in session for just half a year, presumably so students could use the longer holiday period to work. Under current student visa conditions, students can work up to 40 hours a week when their schools are on holiday and 28 hours when they are in session. Although there were previously no rules on how long a school should be in session, the new rule will require schools to be in session for at least 35 weeks a year. "The main duty of a student is to study," said Justice Ministry official Tetsuya Soga, who explained that the new rule is intended as a way to clarify that students should be putting their effort into studying rather than working.

Mapbox, a provider of digital map technology, said it suffered a "malicious edit" by a person who tried to make more than 80 changes to its maps' data in a "disgusting anti-Semitic tirade" across New York and other parts of the world. All were quarantined for human review by the company's artificial-intelligence powered algorithms, and only one edit made it into the actual map for less than an hour before being deleted.

As a teenager in Nigeria, I tried to build an artificial intelligence system. I was inspired by the same dream that motivated the pioneers in the field: That we could create an intelligence of pure logic and objectivity that would free humanity from human error and human foibles. I was working with weak computer systems and intermittent electricity, and needless to say my AI project failed. Eighteen years later--as an engineer researching artificial intelligence, privacy, and machine-learning algorithms--I'm seeing that so far, the premise that AI can free us from subjectivity or bias is also disappointing. We are creating intelligence in our own image.

We introduce an adversarial method for producing high-recall explanations of neural text classifier decisions. Building on an existing architecture for extractive explanations via hard attention, we add an adversarial layer which scans the residual of the attention for remaining predictive signal. Motivated by the important domain of detecting personal attacks in social media comments, we additionally demonstrate the importance of manually setting a semantically appropriate `default' behavior for the model by explicitly manipulating its bias term. We develop a validation set of human-annotated personal attacks to evaluate the impact of these changes.

AAAS analyst assists a human rights organization in gathering data during an exhumation. Against a backdrop of summer heat and a constant roar of distant howler monkeys, a scientific analyst piloted a drone to collect data from a hillside in northern Guatemala. At his side, anthropologists affiliated with a regional human rights group painstakingly cleared soil and roots from human remains in a mass grave. "Remains contorted, overlapping, interlaced, a cruel, tragic mashup of Hieronymus Bosch and H.R. Giger," noted Jonathan Drake, senior program associate of the American Association for the Advancement of Science's Geospatial Technologies Project, summoning images from 15th- and 20th-century artists to describe the nightmarish remnants of an atrocity estimated to have occurred sometime after 1980, during Guatemala's lengthy civil war. Clothing with burnt edges stuck to the bones of some.

The man who hacked into hundreds of iCloud accounts of Hollywood stars and others before leaking their nude photos across the internet has been sentenced. Connecticut man George Garafano became infamous when he stole private photos from people including Jennifer Lawrence and made them available across the internet. He was sentenced to eight months in prison this week, in federal court in Bridgeport. After prison, he must serve three years of supervised release and perform 60 hours of community service. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.

The 18th of March, 2018, was the day tech insiders had been dreading. That night, a new moon added almost no light to a poorly lit four-lane road in Tempe, Arizona, as a specially adapted Uber Volvo XC90 detected an object ahead. Part of the modern gold rush to develop self-driving vehicles, the SUV had been driving autonomously, with no input from its human backup driver, for 19 minutes. An array of radar and light-emitting lidar sensors allowed onboard algorithms to calculate that, given their host vehicle's steady speed of 43mph, the object was six seconds away – assuming it remained stationary. But objects in roads seldom remain stationary, so more algorithms crawled a database of recognizable mechanical and biological entities, searching for a fit from which this one's likely behavior could be inferred. At first the computer drew a blank; seconds later, it decided it was dealing with another car, expecting it to drive away and require no special action. Only at the last second was a clear identification found – a woman with a bike, shopping bags hanging confusingly from handlebars, doubtless assuming the Volvo would route around her as any ordinary vehicle would. Barred from taking evasive action on its own, the computer abruptly handed control back to its human master, but the master wasn't paying attention. Elaine Herzberg, aged 49, was struck and killed, leaving more reflective members of the tech community with two uncomfortable questions: was this algorithmic tragedy inevitable? And how used to such incidents would we, should we, be prepared to get? "In some ways we've lost agency. When programs pass into code and code passes into algorithms and then algorithms start to create new algorithms, it gets farther and farther from human agency. Software is released into a code universe which no one can fully understand."

The opportunities that artificial intelligence (AI) can unlock for our world -- from discovering cures to diseases that kill millions each year to significantly reducing carbon emissions -- are expanding every day -- and is already enabling pathways to financial inclusion, citizen engagement, more affordable healthcare, and many more vital systems and services. The same types of machine learning systems that might have highlighted a certain post in your Facebook newsfeed based on your online activity are being leveraged, for instance, to highlight certain applicants in a hiring process. While public attention often focuses either on the existential threats artificial super-intelligence poses to humanity ("the robots are coming to kill us"), or the opposite salvation narrative (" AI will solve all our problems") there is a more immediate-but less visible- risk that our reliance on ML-driven decision making poses in terms of the reinforcement of systemic bias and discrimination. Machine learning technologies are already making life-altering decisions for human lives on a daily basis. Examples come from the New York Times: "Algorithms can decide where kids go to school… where building code inspections should be targeted, and even what metrics are used to rate a teacher."