A team of computer and legal scientists from the UK worked alongside Daniel Preoțiuc-Pietro – a postdoctoral researcher in natural language processing and machine learning from the University of Pennsylvania – to extract case information published by the ECtHR. They identified English language data sets for 584 cases relating to Articles 3, 6 and 8 of the Convention. Article 3 forbids torture and inhuman and degrading treatment (250 cases); Article 6 protects the right to a fair trial (80 cases) and Article 8 provides a right to respect for one's "private and family life, his home and his correspondence" (254 cases). They then applied an AI algorithm to find patterns in the text. To prevent bias and mislearning, they selected an equal number of violation and non-violation cases.

Tech-savvy farmers have been some of the earliest commercial adopters of drone technology, purchasing 45,000 drones last year alone. But if they were using the drones to check on the condition of their fields, spraying their crops, or keeping tabs on livestock, most of them were technically breaking the law. New U.S. federal rules that went into effect this summer, however, should make it easier for farmers to get a drone's-eye view of their fields. The new rules allow commercial drone operators to get certified via a written test, so long as they fly drones that meet certain weight and altitude guidelines. Before this, operators had to pay for a pilot's license and get a special exemption to use a drone, a slow and cumbersome process.

In his prescient work on investigating the potential use of information technology in the legal domain, Lawlor surmised that computers would one day become able to analyse and predict the outcomes of judicial decisions (Lawlor, 1963). According to Lawlor, reliable prediction of the activity of judges would depend on a scientific understanding of the ways that the law and the facts impact on the relevant decision-makers, i.e., the judges. More than fifty years later, the advances in Natural Language Processing (NLP) and Machine Learning (ML) provide us with the tools to automatically analyse legal materials, so as to build successful predictive models of judicial outcomes. In this paper, our particular focus is on the automatic analysis of cases of the European Court of Human Rights (ECtHR or Court). The ECtHR is an international court that rules on individual or, much more rarely, State applications alleging violations by some State Party of the civil and political rights set out in the European Convention on Human Rights (ECHR or Convention).

Scientists and engineers have created an artificially intelligent algorithm that can parse through legal documents and determine what the likely outcome of a trial will be. In other words, robots aren't here just to take over our jobs, they're here to send us to jail as well. British scientists at UCL developed the algorithm and taught it to understand legalese by feeding the software previous human rights cases. Out of nearly 600 cases involving human rights, torture, privacy and fair trials, the program made the same judicial decisions as the human judges in 79% of cases. If there are any officers of the court feeling a bit nervous about job security right now, there is some good news.

The evolution of the AI programs is reaching many fields: for example, to allow the AI to communicate in an ever more human way, Google's DeepMind developers have started to make it read hundreds of romance novels to help it improve its dialectical skills and develop a minimum of personality. The choice fell on the romantic novels because they have very linear plots and simple narrative schemes but also they are very similar to each other, an element that AI can learn to manage and rework to interact with a human being. The next step is to draft long and elaborate sentences, or even writing entire novels. Not surprisingly, a recent book written by a computer has passed a literary prize screening. The Japanese literary prize Hoshi Shinichi is also open to works produced by artificial intelligences and the jury – without knowing its origin – admitted the book "The day a computer writes a novel", written by the program of a professor of the Hakodate Future University.

Those interested in legal services and technology need to keep their eye on developments in artificial intelligence (AI). In the course of preparing a national US strategy, a committee of the National Science and Technology Council has drafted a report, Preparing for the Future of Artificial Intelligence, which is worth reading as a crib to the latest issues. This is much better written than you might expect from a committee (or, indeed, machine) authorship. The report eschews fanciful speculation about the future and is largely concerned with what it terms'narrow AI' 'which addresses specific application areas such as strategic games, language translation, self-driving vehicles and imagine recognition'. In particular, it looks at'machine learning' which it distinguishes from older'expert system' approaches. Machine learning is concerned to analyse bodies of data and'derive a rule or procedure that explains the data or can predict future data'.

An artificial intelligence algorithm has predicted the outcome of human rights trials with 79 percent accuracy, according to a study published today in PeerJ Computer Science. Developed by researchers from the University College London (UCL), the University of Sheffield, and the University of Pennsylvania, the system is the first of its kind trained solely on case text from a major international court, the European Court of Human Rights (ECtHR). "Our motivation was twofold," co-author Vasileios Lampos of UCL Computer Science told Digital Trends. "It first starts with scientific curiosity." In other words, would it even be possible to create such an AI judge?

The judicial decisions of the European Court of Human Rights (ECtHR) have been predicted to 79% accuracy using an artificial intelligence (AI) method developed by researchers at UCL, the University of Sheffield and the University of Pennsylvania. The method is the first to predict the outcomes of a major international court by automatically analysing case text using a machine learning algorithm. The study behind it was published today in PeerJ Computer Science. "We don't see AI replacing judges or lawyers, but we think they'd find it useful for rapidly identifying patterns in cases that lead to certain outcomes. It could also be a valuable tool for highlighting which cases are most likely to be violations of the European Convention on Human Rights," explained Dr Nikolaos Aletras, who led the study at UCL Computer Science.

Predictive modeling is increasingly being employed to assist human decision-makers. One purported advantage of replacing human judgment with computer models in high stakes settings-- such as sentencing, hiring, policing, college admissions, and parole decisions-- is the perceived "neutrality" of computers. It is argued that because computer models do not hold personal prejudice, the predictions they produce will be equally free from prejudice. There is growing recognition that employing algorithms does not remove the potential for bias, and can even amplify it, since training data were inevitably generated by a process that is itself biased. In this paper, we provide a probabilistic definition of algorithmic bias. We propose a method to remove bias from predictive models by removing all information regarding protected variables from the permitted training data. Unlike previous work in this area, our framework is general enough to accommodate arbitrary data types, e.g. binary, continuous, etc. Motivated by models currently in use in the criminal justice system that inform decisions on pre-trial release and paroling, we apply our proposed method to a dataset on the criminal histories of individuals at the time of sentencing to produce "race-neutral" predictions of re-arrest. In the process, we demonstrate that the most common approach to creating "race-neutral" models-- omitting race as a covariate-- still results in racially disparate predictions. We then demonstrate that the application of our proposed method to these data removes racial disparities from predictions with minimal impact on predictive accuracy.

Multi-billion dollar startup Magic Leap, which is building a cutting-edge augmented reality headset, is currently in a legal battle with the engineer who started its first Silicon Valley office. Court filings reveal new secrets about the company, including a west coast software team in disarray, insufficient hardware for testing, and a secret skunkworks team devoted to getting patents and designing new prototypes -- before its first product has even hit the market. The company believes that Adrian Kaehler and Gary Bradski, two VPs at Magic Leap, tried to rip off its technology and talent to start a new robotics startup. Kaehler and Bradski, who sued the company for wrongful termination earlier this year, say that Magic Leap unfairly robbed them of their shares in Magic Leap and broke their employment contracts. Magic Leap countered by suing the pair for misappropriation of trade secrets.