Artificial intelligence software developed by an Israeli startup has proved in an international study to be quicker and more accurate at analyzing legal documents than experienced lawyers. The software developed by Tel Aviv based LawGeex was able to analyze nondisclosure agreements with more accuracy and speed than 20 experienced lawyers, the results of a collaborative study between leading US institutions and the company show. Get The Start-Up Israel's Daily Start-Up by email and never miss our top stories Free Sign Up As part of the study the researchers compared the work of the experienced lawyers, some with decades of contract experience, to LawGeex's AI software program, and found that the software was able to achieve nearly 10 percent higher accuracy and complete the task in significantly less time. This study marks the first time that AI technology has been tested with a typical task, such as reviewing a nondisclosure agreement, undertaken by lawyers on a daily basis, the company said in a statement. Both the lawyers and LawGeex's AI software were given five previously unseen contracts, which contained 153 paragraphs of technical legal language that were modeled after common nondisclosure agreements.

Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society. In late September, Egyptian authorities began a crackdown against the country's queer communities after fans of Mashrou' Leila, an outspoken Lebanese indie rock group with an openly gay band member, displayed a rainbow flag at the group's concert in Cairo. The government responded quickly in what some activists called the worst campaign against LGBTQIA Egyptians in decades. Security forces arrested more than 85 individuals on a range of charges, including "habitual debauchery." Officials convicted at least 16 and issued sentences ranging from six months to six years in prison (though a handful were later released).

Joy Buolamwini was conducting research at MIT on how computers recognized people's faces, when she started experiencing something weird. Whenever she sat before a system's front-facing camera, it wouldn't recognize her face, even after working for her lighter-skinned friends. But when she put on a simple white mask, the face-tracking animation suddenly lit up the screen. Suspecting a more widespread problem, she carried out a study on the AI-powered facial recognition systems of Microsoft, IBM and Face, a Chinese startup that has raised more than $500 million from investors. Buolamwini showed the systems 1,000 faces, and told them to identify each as male or female.

Perhaps the revelation that predictive policing software is deeply biased against people of color. Or outrage over the use of predictive algorithms to evaluate teachers. Or maybe it'll be something way more pedestrian, like Amazon pushing its own products instead of the cheapest. Either way, we're nearing a moment of reckoning with how AI is regulated by the government, and it's going to be a long road toward reasonable, working legislation on it. This week the AI Now Institute, a leading group studying the topic, published its own proposal.

PSD2 stands for second payment standards directive, and was issued by the EU with the intent to open access to data and services previously only available to banks. In theory, PSD2 went into effect on January 13, and should provide a clear framework for implementation. The difference between theory and practice is small in theory, but big in practice. PSD2 is defined in a set of documents published by the EU, which are to be subsequently implemented as legislation by parliaments in EU countries and enforced by regulating bodies. PSD2 will effectively force financial institutions active in the EU to open up data and functionality previously only available to them to other parties.

The steady increase in innovations relating to new digital technologies, in particular technologies using artificial intelligence, is matched by an upward patenting trend. The European Patent Office recently issued a study on the subject and is preparing a conference in May, while the World Intellectual Property Organization is working on its own in-depth study. However, the current patent system might not be ready for artificial intelligence-related inventions, according to a global standards-setting body. Please login or subscribe to read the full story.

Americans do not agree on guns. Debate is otiose, because we reject each other's facts and have grown weary of each other's arguments. A little more than half the nation wants guns more tightly regulated, because tighter regulation would mean fewer guns, which would mean less gun violence. A little less than half answers, simply: The Supreme Court has found in the Second Amendment an individual right to bear arms. Legally prohibiting or confiscating guns would mean amending the Constitution, which the Framers made hard.

It was always going to be a momentous week for legal technology with the world's largest Global Legal Hackathon taking place in over 40 cities. But for the Aussies, it was a double whammy with ALTA's inaugural event and the launch of its Demo Day series, showcasing some of Australia's best legal technology start-ups all on one stage, in very lawyer-like six minute-long demonstrations. Hosted by Macquarie Bank at their Melbourne and Sydney headquarters and also supported by industry heavyweights Janders Dean and Elevate Services, the dual city tour was attended by a diverse variety of industry stakeholders including in-house legal departments, government, law societies, academics, multi-nationals and'techies', whilst Big Law right through to NewLaw and even'tiny' law were all well represented. What emerged from the two days was the incredible depth of our home-grown talent with many commenting on the impressive diversity; the companies showing off everything from data-driven tools for in-house teams to even an AI powered'law firm without lawyers'; but most importantly, a true sense of community was born. The ideas, conversations and relationships that transpired during the coffee breaks, in the conference rooms, and that continued out the doors well after the final demo will have a lasting effect.

Following related work in law and policy, two notions of disparity have come to shape the study of fairness in algorithmic decision-making. Algorithms exhibit treatment disparity if they formally treat members of protected subgroups differently; algorithms exhibit impact disparity when outcomes differ across subgroups, even if the correlation arises unintentionally. Naturally, we can achieve impact parity through purposeful treatment disparity. In one thread of technical work, papers aim to reconcile the two forms of parity proposing disparate learning processes (DLPs). Here, the learning algorithm can see group membership during training but produce a classifier that is group-blind at test time. In this paper, we show theoretically that: (i) When other features correlate to group membership, DLPs will (indirectly) implement treatment disparity, undermining the policy desiderata they are designed to address; (ii) When group membership is partly revealed by other features, DLPs induce within-class discrimination; and (iii) In general, DLPs provide a suboptimal trade-off between accuracy and impact parity. Based on our technical analysis, we argue that transparent treatment disparity is preferable to occluded methods for achieving impact parity. Experimental results on several real-world datasets highlight the practical consequences of applying DLPs vs. per-group thresholds.

Americans do not agree on guns. Debate is otiose, because we reject each other's facts and have grown weary of each other's arguments. A little more than half the nation wants guns more tightly regulated, because tighter regulation would mean fewer guns, which would mean less gun violence. A little less than half answers, simply: The Supreme Court has found in the Second Amendment an individual right to bear arms. Legally prohibiting or confiscating guns would mean amending the Constitution, which the Framers made hard.