Newswire) VSBLTY Groupe Technologies Corp. (CSE: VSBY) (5VS.F) (VSBGF), a leading retail software and technology company, is teaming with Energetika, an international provider of "intelligent lighting" solutions, to install safety lighting and integrated security to Mexico City, and other Latin American cities designated as a "Smart City." Accessibility, habitability, sustainability, air quality, noise levels, energy, health and economic vitality are among the elements necessary to be selected as a "Smart City." Energetika is a leading provider of smart lighting solutions for economically efficient applications that incorporate security. Energetika chose VSBLTY to provide security technology that includes crowd analytics and facial recognition for residential, commercial and governmental applications. VSBLTY technology provides enhanced customer engagement and audience measurement using machine learning and computer vision.

Beyond the classroom curriculum, many law schools are designing experiential modes of introducing law students to artificial intelligence. At Georgia State University School of Law, for instance, the Legal Analytics and Innovation Initiative gives law students a chance to collaborate closely with computer science and business students at the same university to design complex technologies that solve previously unsolvable legal problems (such as predicting to a high degree of accuracy how a particular judge will rule in cases defined by a large set of parameters). This kind of work not only has the potential to be a flow-through to the legal practitioner space, but could over time become a mechanism for law schools to "spin out" the kinds of revenue-generating start-up businesses that are a common facet of life science departments at research universities. These programs have also been shown (according to the programs' own statistics) to help law students land jobs at higher rates than the overall student body, no doubt because the intersection of technology and law is a rare and valuable skillset in the eyes of employers.

As AI algorithms--and the computing power that drives them--improve year-on-year, their ability to positively transform the world in which we live is unquestionable. In fact, PwC predicts that AI could contribute up to $15.7 trillion to the global economy by 2030. Indeed, as many as one-in-five (20 percent) of the 1,000 US organisations recently surveyed by PwC had plans to implement AI enterprise-wide in 2019. The PwC research also reveals how companies are increasingly initiating AI models at the very core of their production processes, in a bid to enhance operational decision-making and provide forward-looking intelligence to people in every function throughout the business. To many, this move to AI is no surprise. After all, robots have been used for years in many manufacturing disciplines, so the progression to AI seems like a logical next step.

Developing learning methods which do not discriminate subgroups in the population is a central goal of algorithmic fairness. One way to reach this goal is by modifying the data representation in order to meet certain fairness constraints. In this work we measure fairness according to demographic parity. This requires the probability of the possible model decisions to be independent of the sensitive information. We argue that the goal of imposing demographic parity can be substantially facilitated within a multitask learning setting. We leverage task similarities by encouraging a shared fair representation across the tasks via low rank matrix factorization. We derive learning bounds establishing that the learned representation transfers well to novel tasks both in terms of prediction performance and fairness metrics. We present experiments on three real world datasets, showing that the proposed method outperforms state-of-the-art approaches by a significant margin.

Microsoft was one of the earliest companies to begin discussing and advocating for an ethical perspective on artificial intelligence. The issue began to take off at the company in 2016, when CEO Satya Nadella spoke at a developer conference about how the company viewed some of the ethical issues around AI, and later that year published an article about these issues. Nadella's primary focus was on Microsoft's orientation toward using AI to augment human capabilities and building trust into intelligent products. The next year, Microsoft's R&D head Eric Horvitz partnered with Microsoft's president and chief legal officer Brad Smith to form Aether, a cross-functional committee addressing AI and ethics in engineering and research. With these foundations laid, in 2018, Microsoft established a full-time position in AI policy and ethics.

SAP SE (NYSE: SAP) today said it will offer a new course on the openSAP platform that focuses on the ethical implications when developing and interacting with artificial intelligence (AI). Creating Trustworthy and Ethical Artificial Intelligence, offered June 25 through July 24, is geared toward all leaders, professionals, developers and general users of AI technology. "The potential for AI and machine learning is great, and the technology has already had significant impact in automating tasks and efficiently analyzing data sets," said Bernd Welz, chief knowledge officer, SAP. "As this technology continues to evolve and becomes further engrained in our society, it's important that we take the necessary steps to ensure that its development and continued application are carried out in an ethical way. Through this course, we're showing learners how they can keep ethics in the forefront when developing AI- and machine learning–enabled technologies."

Evisort, an artificial intelligence (AI) technology company, today announced the appointment of legal tech veteran Alexander Su (Alex) as its Director of Business Development. Su previously led the sales team at Logikcull, an eDiscovery automation company that grew revenue from 0 to $10M in just 19 months. Su's move is the latest in a recent trend of leadership talent being drawn to Evisort due to its explosive growth this year. Over the past decade, Su has been uniquely positioned to observe the evolution of legal technology. Earlier in his career, Su managed numerous discovery workflows as an associate at Sullivan & Cromwell.

AI technology is making headlines in a wide range of industries including financial services and medical, but legal AI may not immediately come to mind for many. However, AI is already transforming the legal sector in many ways, primarily because it is streamlining traditionally cumbersome processes and allowing professionals to focus on higher-level tasks. For those interested in developing legal machine learning applications, we at Lionbridge AI have scoured the web to put together a collection of the best publicly available legal datasets. In case you missed our previous dataset compilations, you can find them all here. Still can't find the custom data you need to train your model?

Human trafficking is a crime that takes place largely in the shadows. Victims, who are mostly women and children, often lack legal documentation in the country where they are forced to work or perform sex acts, and many fear reprisals if they go to authorities. Perpetrators, for obvious reasons, take great pains to conceal their behavior by laundering money and keeping their operations quiet. And others who engage in trafficking-related criminal activity -- such as individuals looking to connect with trafficked sex workers -- also have powerful incentives to hide their participation. Recently, law enforcement agencies and organizations that help victims of human trafficking have begun using artificial intelligence tools to overcome this lack of visibility.

At ROSS, we're always talking to lawyers about how artificial intelligence is changing their practice. Our discussions are usually focused on AI and legal research. But sometimes the conversation turns to applications that might have seemed far-fetched a couple of years ago but now don't seem quite so crazy. A recent conversation with plaintiff's personal injury lawyers turned toward the application of prediction markets to settlement negotiations. The gist of the idea was that crowd-sourced predictions on settlement amounts could move unreasonable parties into reasonable settlement range. Although the theory that prediction markets could assist in litigation settlement has been around for awhile, it hasn't gained much practical traction.