Just when we thought it was safe to go back in the water, a new threat has emerged to equal employment opportunity as employers base hiring decisions on artificial intelligence powered video and game-based "pre-employment" assessments of job candidates. The Electronic Privacy Information Center, a public interest research center based in Washington, D.C., recently asked the Federal Trade Commission to investigate HireVue, a recruiting company based in Utah that purports to evaluate a job applicant's job qualifications through online "video interview" and/or "game-based challenge." According to its web site, HireVue has more than 700 customers worldwide including over one-third of the Fortune 100 and such leading brands such as Unilever, Hilton, JP Morgan Chase, Delta Air Lines, Vodafone, Carnival Cruise Line, and Goldman Sachs. The company states it has hosted more than ten million on-demand interviews and one million assessments. The EPIC complaint follows a wave of lawsuits in recent years charging that employers are using software algorithms to discriminate against older workers by targeting internet job advertisements exclusively to younger workers.

In this special guest feature, Stewart Dunlop, a content manager working with LegalZoom, highlights the fact that artificial intelligence has been predicted to be of value to many different industries, and perhaps one of the most feasible applications will be within legal services. This is due to several factors, which will be explained further in the article, but perhaps the most important is the fact that one of AI's biggest strengths is data collection and analysis. Stewart is a full-time content writer and part-time footballer and reader. Artificial intelligence has been predicted to be of value to many different industries, and perhaps one of the most feasible applications will be within legal services. This is due to several factors, which we will explain further in the article, but perhaps the most important is the fact that one of AI's biggest strengths is data collection and analysis.

When you think about automation, there's a good chance you think about robots in a factory or a warehouse. That's partially because a lot of automation that is starting to be utilized more and more has to do with robotics. However, as a new report from the Brookings Institute explains, it's not just blue collar jobs associated with physical labor that are under threat. Developments in robotics will contribute to the loss of largely blue-collar jobs, but will also AI threaten the high-paying jobs many of us are striving to one day obtain, according to the report. Robert Maxim, a research associate in the Metropolitan Policy Program at Brookings, tells Inverse automation is going to impact pretty much every kind of job.

As tensions rise between the US and China, there's been some chatter of an AI arms race that would see each country scrambling to get and retain some kind of advantage in the field. But Microsoft cofounder Bill Gates, who spoke at the Bloomberg New Economy Forum in Beijing on Thursday, says he has some difficulty understanding how separating or limiting the sharing of scientific research would even work. "You can't, you know, carry around little notes to each other saying don't give this to someone because their grandmother is Chinese," he said. Gates said that the US has long benefitted from openly sharing scientific research, and that it remains a huge advantage, especially within the field of artificial intelligence. "AI is very hard to put back in the bottle and whoever has the open system will so vastly get ahead," Gates said.

The Internet of Things (IoT) is actively shaping both the industrial and consumer worlds. Smart tech finds its way to every business and consumer domain there is -- from retail to healthcare, from finances to logistics -- and a missed opportunity strategically employed by a competitor can easily qualify as a long-term failure for companies who don't innovate [3]. The year 2020 will hit all 4 components of IoT Model: Sensors, Networks (Communications), Analytics (Cloud), and Applications, with different degrees of impact. By 2020, the Internet of Things (IoT) is predicted to generate an additional $344B in revenues, as well as to drive $177B in cost reductions. IoT and smart devices are already increasing the performance metrics of major US-based factories.

Artificial intelligence and automation are responsible for a growing number of decisions by pubic authorities in areas like criminal justice, security and policing and public administration, despite having proven flaws and biases. Facial recognition systems are entering public spaces without any clear accountability or oversight. Lawyers must play a greater role in ensuring the safety and accountability of advanced data and analytics technologies, says Karen Yeung at the University of Birmingham. The dream of artificial intelligence stretches back seven decades, to a seminal paper by Alan Turing. But only recently has AI been commercialized and industrialized at scale, weaving its way into every nook and cranny of our lives.

A few months ago, I sent my dad the article 20 Top Lawyers Were Beaten by Legal AI in a Controlled Study, which (as the title suggests) discusses a study on how AI can be applied to the field of law, and how it performs against professional lawers. An implication of this article is the potential to replace lawyers with AI for many common legal needs, such as contract review or writing wills. It's an interesting article and application of AI, which I spend a lot of time thinking about. It might seem pretty innocent that I shared it with my dad, and it would be, except that my dad is a lawyer. Yes, I was kind of trying to get a rise out of him (it's all affectionate, I promise).

Assessing risk for voluminous legal documents such as request for proposal; contracts is tedious and error prone. We have developed "risk-o-meter", a framework, based on machine learning and natural language processing to review and assess risks of any legal document. Our framework uses Paragraph Vector, an unsupervised model to generate vector representation of text. This enables the framework to learn contextual relations of legal terms and generate sensible context aware embedding. The framework then feeds the vector space into a supervised classification algorithm to predict whether a paragraph belongs to a per-defined risk category or not. The framework thus extracts risk prone paragraphs. This technique efficiently overcomes the limitations of keyword-based search. We have achieved an accuracy of 91% for the risk category having the largest training dataset. This framework will help organizations optimize effort to identify risk from large document base with minimal human intervention and thus will help to have risk mitigated sustainable growth. Its machine learning capability makes it scalable to uncover relevant information from any type of document apart from legal documents, provided the library is per-populated and rich.

This loss discrepancy across groups is especially problematic in critical applications that impact people's lives (Berk, 2012; Chouldechova, 2017). Despite the vast literature on removing loss discrepancy (Hardt et al., 2016; Khani et al., 2019; Agarwal et al., 2018; Zafar et al., 2017), the direct removal of loss discrepancy might introduce other problems such as intragroup loss discrepancy (Lipton et al., 2018) and adverse long-term impacts (Liu et al., 2018). Therefore, it is important to understand the source of loss discrepancy. Why do such loss discrepancies exist? The literature generally studies sources of loss discrepancy due to an "information deficiency" of one group--that is, one group has, for example, more noise (Corbett-Davies et al., 2017), lessPreliminary work, under review.

For many NLP applications, such as question answering and summarisation, the goal is to select the best solution from a large space of candidates to meet a particular user's needs. To address the lack of user-specific training data, we propose an interactive text ranking approach that actively selects pairs of candidates, from which the user selects the best. Unlike previous strategies, which attempt to learn a ranking across the whole candidate space, our method employs Bayesian optimisation to focus the user's labelling effort on high quality candidates and integrates prior knowledge in a Bayesian manner to cope better with small data scenarios. We apply our method to community question answering (cQA) and extractive summarisation, finding that it significantly outperforms existing interactive approaches. We also show that the ranking function learned by our method is an effective reward function for reinforcement learning, which improves the state of the art for interactive summarisation.