This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These mechanisms of classification all frequently rely on computational algorithms, and in many cases on machine learning algorithms to do this work. In this article, I draw a distinction between three forms of opacity: (1) opacity as intentional corporate or state secrecy, (2) opacity as technical illiteracy, and (3) an opacity that arises from the characteristics of machine learning algorithms and the scale required to apply them usefully. The analysis in this article gets inside the algorithms themselves. I cite existing literatures in computer science, known industry practices (as they are publicly presented), and do some testing and manipulation of code as a form of lightweight code audit. I argue that recognizing the distinct forms of opacity that may be coming into play in a given application is a key to determining which of a variety of technical and non-technical solutions could help to prevent harm. This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These are just some examples of mechanisms of classification that the personal and trace data we generate is subject to every day in network-connected, advanced capitalist societies. These mechanisms of classification all frequently rely on computational algorithms, and lately on machine learning algorithms to do this work. Opacity seems to be at the very heart of new concerns about'algorithms' among legal scholars and social scientists. The algorithms in question operate on data. Using this data as input, they produce an output; specifically, a classification (i.e. They are opaque in the sense that if one is a recipient of the output of the algorithm (the classification decision), rarely does one have any concrete sense of how or why a particular classification has been arrived at from inputs.

A major chore of obtaining planning permission for a new development is dealing with neighbouring properties' "right to light". This involves obtaining and examining the title deeds of all properties likely to be affected and drafting standard notification letters. A city-centre development might require the examination of hundreds of title deeds. Traditional law firms give this routine and repetitive work to trainees or paralegals. However, it is exactly the sort of work that lends itself to artificial intelligence or AI-based automation.

The world's first artificial intelligence lawyer has been employed by a law firm in the US, which will use the robot to assist its various teams in legal research. The robot called'ROSS' is built upon Watson, IBM's cognitive computer. With the support of Watson's cognitive computing and natural language processing capabilities, lawyers can ask ROSS their research question and the robot reads through the law, gathers evidence, draws inferences and returns highly relevant, evidence-based answers. ROSS also monitors the law around the clock to notify users of new court decisions that can affect a case. The programme continually learns from the lawyers who use it to bring back better results each time.

Frank Pasquale, professor of law at the University of Maryland, reflects on the roles of machines and machine learning in today's society, and to what extent'opaque' algorithmic systems should be subject to human oversight. The theme of "technics out of control" animated several other mid-20th-century films. Common themes emerged, variations on the Frankenstein or "sorcerer's apprentice" myths. For example, in Colossus: The Forbin Project, a machine assures perpetual peace, but appears capable of controlling much more than nuclear arsenals. It sternly warns one of its creators that "freedom is just an illusion" and "In time you will come to regard me not only with respect and awe, but with love."

Skye Gould/Tech InsiderA well-dressed humanoid not named Ross. Lawyers often get a bad reputation for being slimy and conniving (deservedly or not), but ROSS has neither of those qualities. Ask ROSS to look up an obscure court ruling from 13 years ago, and ROSS will not only search for the case in an instant -- without contest or complaint -- but it'll offer opinions in plain language about the old ruling's relevance to the case at hand. Just about the only thing it can't do is fetch coffee. Not that anyone should blame it, seeing as ROSS is a piece of artificial intelligence software.

HANNOVER, GERMANY - MARCH 02: Robots play football in a demonstration of artificial intelligence at the stand of the German Research Center for Artificial Intelligence (Deutsches Forschungszentrum fuer Kuenstliche Intelligenz GmbH) at the CeBIT Technology Fair on March 2, 2010 in Hannover, Germany. CeBIT will be open to the public from March 2 through March 6. (Photo: Sean Gallup/Getty Images) Artificial intelligence or AI is among the most favorite subjects today. With AI's versatility and flexibility, you can find them anywhere. There are already companies run by AI and factories with robots operating the machines. But did you know that AI robots also suffer stereotypical sexism in the workplace?

A career in law and extremely long hours tend to go hand in hand. Partly of course it's about proving your commitment, but being a lawyer also involves an awful lot of grunt work - spending hours and hours looking through past case law to help your firm determine how to fight a current case. It's this time consuming, labour intensive research aspect of the legal system that Andrew Arruda, co-founder and chief executive of tech start-up Ross Intelligence, believes its invention can address. The AI (or artificial intelligence) legal research system allows lawyers to type in a question - much in the same way they'd ask a colleague - and bring up relevant examples of what has happened in previous US legal cases, essentially at the touch of a button. "Lawyers may know the law and where it stands on a particular issue today but many cases come out and it can change that so they're always looking into the past to build the future. "The issue with that is there's just millions of cases.

Law school, the default location for America's brightest unemployed Liberal Arts graduates and the worst decision a 20-something can make in the modern era just became an even worse bargain, if that's possible. We're not talking about a surge in LSAT applicants, beginning in 2008, that led to the creation of InfiLaw Corporation scam-law-schools like Charlotte, Florida Coastal, or Arizona Summit, the latter of which boasts an average bar passage rate of below 31% and an average cost of attendance exceeding 250,000. We are not even speaking about a profession that requires the completion of at least seven years of college,followed by an expensive test and an invasive background check, just for the benefit of staring down the barrel of a 15.5 percent unemployment rate and roughly 200,000 in non-dischargeable student debt. One might even guess that we're talking about how recent studies show that over 40% of law school students suffer from clinical depression, with those unhappy figures only becoming worse as graduates enter a profession with a suicide rate nearly ten times above the national norm. Instead, we are talking about the latest and greatest idea among the old-timers; those old ones who have already won the legal profession lottery, allowing them to seize an upper-middle class lifestyle after matriculating from law schools referred to as'third-tier toilets,' or attending bastions of prestigious opportunity, like Harvard and Yale.

The report suggests that the increasing volume of digital records makes techniques leveraging machine learning the most cost-effective options to conduct review. Predictive coding or technology-assisted review (TAR), for instance, harnesses supervised machine learning to predict the responsiveness of documents based on prior coding decisions. From our experience, most savvy practitioners see this type of machine learning as an obvious way to increase efficiencies in the review process, but a number of factors have limited adoption. As a result, there is a conspicuous "consumption gap" in legal technology, which emerges from the difference in the current use of technology versus its capabilities. A 2015 PC –TAR Focus Report prepared by the eDJ Group noted that counsel and management often resist analytics technologies due to their limited knowledge of software capabilities, limitations, potential costs, and applications.

Skye Gould/Tech InsiderA well-dressed humanoid not named Ross. Lawyers can get a bad reputation for being slimy and conniving, but ROSS has neither of those qualities. Ask ROSS to look up an obscure court ruling from 13 years ago, and ROSS will not only search for the case in an instant -- without contest or complaint -- but it'll offer opinions in plain language about the old ruling's relevance to the case at hand. Just about the only thing it can't do is fetch coffee. Not that anyone should blame it, seeing as ROSS is a piece of artificial intelligence software.