Lawyers are using artificial intelligence tools for automating tasks, such as contract review and sorting through electronic discovery documents, according to the article. But higher level tasks, especially those that require experience, will take a while, lawyers and other experts told the newspaper. Professor Dana Remus of the University of North Carolina School of Law and labor economist Frank Levy of the Massachusetts Institute of Technology published a paper on the automation of legal work in 2016 and concluded that although the automation of legal tasks reduces the amount of work lawyers must do, it's not enough to put lawyers out of business. Their paper said that if large law firms adopt new legal technology immediately, those lawyers would lose 13 percent of their current work hours. But the authors said it's more realistic to assume that this would happen over five years, which would result in closer to a 2.5 percent reduction in hours per year.

"The remedy is more speech, not enforced silence," wrote US Supreme Court justice Louis Brandeis in 1927 in his defence of freedom of speech. Ninety years on, his position is often taken as read: in the marketplace of ideas, eventually the truth will out. So it's no surprise that many were aghast when, last week, Germany's justice minister introduced a draft law that would fine social media companies, including Facebook and Twitter, up to €50 million if they failed to remove hate speech within 24 hours of a complaint.

FBI bosses have been grilled over a secret facial recognition program that allows agents to access photos of tens of millions of Americans. The agency came under fire for its unrestricted access to ID photos in 18 states to help identify potential suspects - including innocent people who have never been charged with a crime. Privacy advocates suggested the technology fosters racial bias and compared the system to'Nazi Germany', at a congressional hearing yesterday. The system allows agents to access and algorithmically match the photos of tens of millions of Americans. More than 400million pictures of Americans' faces are kept in local, state and federal law enforcement facial recognition systems, the Government Accountability Office reported last year.

Algorithmic decision making systems are ubiquitous across a wide variety of online as well as offline services. These systems rely on complex learning methods and vast amounts of data to optimize the service functionality, satisfaction of the end user and profitability. However, there is a growing concern that these automated decisions can lead, even in the absence of intent, to a lack of fairness, i.e., their outcomes can disproportionately hurt (or, benefit) particular groups of people sharing one or more sensitive attributes (e.g., race, sex). In this paper, we introduce a flexible mechanism to design fair classifiers by leveraging a novel intuitive measure of decision boundary (un)fairness. We instantiate this mechanism with two well-known classifiers, logistic regression and support vector machines, and show on real-world data that our mechanism allows for a fine-grained control on the degree of fairness, often at a small cost in terms of accuracy. A Python implementation of our mechanism is available at fate-computing.mpi-sws.org

In February 2016, a London court supported the use of predictive coding software in a legal disclosure process, which often involves lawyers receiving huge volumes of documents from those representing the other side in a case. This email address is already registered. By submitting my Email address I confirm that I have read and accepted the Terms of Use and Declaration of Consent. By submitting your personal information, you agree that TechTarget and its partners may contact you regarding relevant content, products and special offers. You also agree that your personal information may be transferred and processed in the United States, and that you have read and agree to the Terms of Use and the Privacy Policy.

A team at Mississippi State University is suggesting using robots to question children in investigations of child abuse. But not everyone is convinced. Children's accounts are often vital evidence in cases of abuse. But even specially trained police interviewers can find it tough to stay neutral when talking to children. This can result in leading questions and bad evidence, because children can be very suggestible to saying what they think someone wants to hear.

Legal research company Casetext has raised $12 million in a new round of funding. The money will be used to expand its software platform that offers insights into cases cited in legal documents and further develop CARA (Case Analysis Research Assistant), an AI-powered assistant for lawyers. Using natural language understanding, Casetext scans the text of legal briefs to locate and analyze case citations. The company also offers access to 10 million court cases and statutes annotated by a community of litigators. The $12 million funding round was led by Canvas Ventures, with participation from Union Square Ventures, 8VC, and Red Sea Ventures.

Nonetheless, arguing that First Amendment coverage may extend to strong A.I. speakers raises a number of legitimate concerns. If A.I. is protected, why not protect speech by cats or by parts of nature, like waves? Well, for one thing, unlike a meow or a crashing sound, A.I. speech uses words and is therefore more likely to be understood as conveying a message. A.I. is also more likely to be central to some human communications effort, supplanting human communication. In other words, we often construct A.I. to serve an essentially communicative function.

Experts say robots will be able to do'most jobs' within 30 years. That kind of prediction makes current international trade spats seem paltry in comparison to the brewing demise of humanity's jobs market. China's Changying Precision Technology recently replaced 90% of its employees with robotics systems and saw a boost in productivity in doing so. Japan even has a hotel run by robots. When it comes to the implications of the impending Johnny 5 era, Mady Delvaux is one of the European Parliament's most vocal MEPs.

SAP SE (NYSE: SAP) today announced it was ranked as a leader in "The Forrester Wave: Predictive Analytics And Machine Learning Solutions, Q1 2017." According to Forrester, "SAP offers comprehensive data science tools to build models, but it is also the biggest enterprise application company on the planet. This puts SAP in a unique position to create tools that allow business users with no data science knowledge to use data-scientist-created models in applications. SAP's solution offers the data tools that enterprise data scientists expect, but it also offers distinguished automation tools to train models." A finding in the report is that the predictive analytics and machine learning (PAML) market is forecasted to experience a 15 percent compound annual growth rate (CAGR) through 2021.*