Government & the Courts



The Only Way to Stay Ahead of the Robots Taking Over Our Jobs Is to

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This post was originally published on The Business Insider. Goff added that technological advances today are progressing far more rapidly than they did during the shift from agriculture to manufacturing. Even if it's technologically possible, you've got to get through state laws, local laws, federal laws. This post was originally published on The Business Insider.


Predicting Supreme Court Decisions Using Artificial Intelligence

@machinelearnbot

The approach to using data to inform legal predictions (as opposed to pure lawyerly analysis) has been largely championed by Prof. Katz – something that he has dubbed "Quantitative Legal Prediction" in recent work. Many of these approaches employ "Machine Learning" techniques to engage in prediction. Pioneering work in the area of quantitative legal prediction began in 2004 with a seminal project by Prof. Ted Ruger (U Penn), Andrew D. Martin (now dean at U Michigan) and other collaborators, employing statistical methods to predict Supreme Court outcomes. The authors applied this algorithmic approach to examine data about past Supreme Court cases found in the Supreme Court Database.


Dara Khosrowshahi: who is the man chosen as Uber's next CEO?

The Guardian

The man designated as Uber's new chief executive left Tehran for the US aged nine on the eve of the Iranian revolution, and became a driving force behind the success of the online travel company Expedia. Senior executives have departed, while the company has faced accusations of sexual discrimination and harassment, and legal headaches including an intellectual property dispute with Waymo, the company operating Google's self-driving car. After his family left Tehran, Khosrowshahi grew up in New York state, spending six of his teenage years raised solely by his mother after his father was detained in Iran, having returned to take care of his own father. He moved from the investment bank Allen & Company to management at InterActive Corp, which acquired Expedia and appointed Khosrowshahi as chief executive in 2005.


Timber! Top Texas Republicans Look to Axe Local Tree Rules

U.S. News

A home once built by Texas Gov. Greg Abbott is seen in Austin, Texas, Thursday, Aug. 10, 2017. While serving as state attorney general in 2011, Abbott tore down his Austin home and built the new one. City records show Abbott was allowed to do so as long as he didn't damage the root systems of two large pecan trees, though roots were eventually damaged in the renovations.


Automated decision making shows worrying signs of limitation

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The service also uses data analysis to target fire safety advice, and has found correlations between high risks of accidental home fires and single-person households, social renting, unemployment, smoking and black and Afro-Caribbean ethnicity. The US Supreme Court recently declined to review the Wisconsin supreme court's ruling in favour of Compas' use in Loomis' case, although the Electronic Privacy Information Centre is involved in several other legal challenges it calls a lack of algorithmic transparency. In the 1990s, Rich Caruana, then a graduate student at Carnegie Mellon University, worked on training a neural net machine learning system to predict the probability of death for pneumonia patients. More recent research found this data similarly suggested that chest pain and heart disease patients were less vulnerable to pneumonia.


Artificial intelligence prevails at predicting Supreme Court decisions

@machinelearnbot

Artificial intelligence can predict Supreme Court decisions better than some experts. Decision outcomes included whether the court reversed a lower court's decision and how each justice voted. The model then looked at the features of each case for that year and predicted decision outcomes. "Every time we've kept score, it hasn't been a terribly pretty picture for humans," says the study's lead author, Daniel Katz, a law professor at Illinois Institute of Technology in Chicago.


Big data is used to sentence criminals, can algorithms predict future risk?

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When the judge weighed Loomis' sentence, he considered an array of evidence, including the results of an automated risk assessment tool called COMPAS. Then, developers create a statistical algorithm that weighs stronger predictors more heavily than weaker ones. Algorithms such as COMPAS cannot make predictions about individual defendants, because data-driven risk tools are based on group statistics. The Supreme Court might helpfully opine on these legal and scientific issues by deciding to hear the Loomis case.


Why using AI to sentence criminals is a dangerous idea

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The report was produced by a software product called Compas, which is marketed and sold by Nortpointe Inc to courts. What we do know is that the prosecutor in the case told the judge that Loomis displayed "a high risk of violence, high risk of recidivism, high pretrial risk." The Wisconsin Supreme Court convicted Loomis, adding that the Compas report brought valuable information to their decision, but qualified it by saying he would have received the same sentence without it. A report on Compas from ProPublica made clear that black defendants in Broward County Florida "were far more likely than white defendants to be incorrectly judged to be at a higher rate of recidivism".


Machine learning algorithm can predict Supreme Court outcomes

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The algorithm analysed the US Supreme Court Database, which holds data on court cases dating back to 1791. They used the US Supreme Court Database, which holds information on court cases dating back to 1791. Based on this data, the algorithm could correctly predict 70.2 per cent of the court's 28,000 decisions, and 71.9 per cent of the justices' 240,000 votes from 1816 to 2015. The algorithm analysed the US Supreme Court Database, which holds information on court cases dating back to 1791.