In 2016, the majority of full-time employed women in the U.S. earned significantly less than comparable men. The extent to which women were affected by gender inequality in earnings, however, depended greatly on socio-economic characteristics, such as marital status or educational attainment. In this paper, we analyzed data from the 2016 American Community Survey using a high-dimensional wage regression and applying double lasso to quantify heterogeneity in the gender wage gap. We found that the gap varied substantially across women and was driven primarily by marital status, having children at home, race, occupation, industry, and educational attainment. We recommend that policy makers use these insights to design policies that will reduce discrimination and unequal pay more effectively.

An autonomous system is constructed by a manufacturer, operates in a society subject to norms and laws, and is interacting with end-users. We address the challenge of how the moral values and views of all stakeholders can be integrated and reflected in the moral behaviour of the autonomous system. We propose an artificial moral agent architecture that uses techniques from normative systems and formal argumentation to reach moral agreements among stakeholders. We show how our architecture can be used not only for ethical practical reasoning and collaborative decision-making, but also for the explanation of such moral behavior.

The question addressed in this paper is: If we present to a user an AI system that explains how it works, how do we know whether the explanation works and the user has achieved a pragmatic understanding of the AI? In other words, how do we know that an explanainable AI system (XAI) is any good? Our focus is on the key concepts of measurement. We discuss specific methods for evaluating: (1) the goodness of explanations, (2) whether users are satisfied by explanations, (3) how well users understand the AI systems, (4) how curiosity motivates the search for explanations, (5) whether the user's trust and reliance on the AI are appropriate, and finally, (6) how the human-XAI work system performs. The recommendations we present derive from our integration of extensive research literatures and our own psychometric evaluations.

Me: "Alexa, tell me what will happen in 2019." Amazon AI: "Do you want to open'this day in history'?" Me: "Alexa, give me a prediction for 2019." Amazon AI: "The crystal ball is clouded, I can't tell." My conversation with Amazon's "smart speaker" or "intelligent voice assistant" just about sums up the present state of "artificial intelligence" (AI) at home, the office, and the factory: Try a few times and sooner or later you will probably get the correct action the human intelligence behind it programmed it to perform. What will be the state of AI in 2019? The following list features 120 senior executives involved with AI, all peering into their not-so-clouded crystal ball, and promising less hype and more practical, precise, and narrow AI. "Self-Driving Finance is a practical implementation of AI that is already used in one form or another by millions of bank customers around the globe and will only get better in the coming years. Based on projects that are currently underway with ...

But using technology to optimise previously complex, time consuming processes is not a new concept. However, employing artificial intelligence (AI) for the analysis and management of traditionally unstructured information, has the potential to not just unlock more value for users but also provide insights that have not been possible before. That is why artificial intelligence for the lawyer or for legal workers is beginning to gain traction. Indeed, the future of legal profession may well be AI and the impact of technology on legal profession will be significant indeed. Artificial intelligence for the lawyer will help change contract management, with the potential to deliver such a significant transformation to this area because contracts are the foundation of a company's commercial relationships.

Luckily I've had the opportunity before for you and I to have a couple of conversations about DataSeers, and I was wondering if you could give our audience a brief overview of your organization and its role within the payments industry? As the name suggests, we are data seers, which means we see through data. If you look at the payments industry today, it is generating large volumes of data. It's creating a large variety of data because payments are very different when they come from different providers, different processors, and so on and so forth. And it's also coming very fast, so that volume, velocity, and variety creates a toxic mix for banks and other companies in the payments ecosystem to handle.

The president of Microsoft has called for greater government regulation of AI facial recognition technology, because of the risk of it discriminating against women and people of colour. In a rare incident of a tech giant calling for greater government scrutiny, Brad Smith said such regulation would help avoid "a commercial race to the bottom, with tech companies forced to choose between social responsibility and market success". The comments of Mr Smith, 59, which were released at the same time as a report by a research group consisting of both Microsoft and Google employees also calling for more regulation, are especially noteworthy because of the controversy the company triggered earlier this year over its AI work. In June, the company's general manager Tom Keane, wrote how proud Microsoft was to be working with the US Immigration and Customs Enforcement agency (ICE) to use facial recognition technology to help identify immigrants and process applications. In a blog post about Azure Government, a programme designed to allow government agencies upload information to the computing cloud, he said: "The agency is currently implementing transformative technologies for homeland security and public safety, and we're proud to support this work with our mission-critical cloud." The comments were made as the Trump administration and ICE were facing intense criticism from human rights advocates and others for the way migrant families were being broken up and separated at the US-Mexico border.

George Orwell's nightmare vision of the future in his novel 1984 could become reality unless governments are curbed, claims Microsoft's president. Brad Smith has warned that facial recognition technology will lead to dystopian state powers unless new measures are adopted. Microsoft say it has developed a new set of principles for deployment of the technology, called for new laws and industry rivals to follow suit. Mr Smith made the announcement at a Brookings Institution speech and an accompanying blog post, saying it was urgent to begin placing limits on facial recognition to avoid a surveillance state. 'We must ensure that the year 2024 doesn't look like a page from the novel '1984,'' Mr Smith said.

Whitehall is to spend £1 million researching how developments in artificial intelligence can boost productivity in the legal profession. The cash is part of a bundle of £3 million in funding that targets technological advancements across insurance and other financial services as well as the law, with the money being split equally between the three areas. Ministers from the Department for Business, Energy and Industrial Strategy said that the research project would study "how AI can be put to use in legal services and how to unlock its potential for good". The project would involve academics, lawyers, businesses and programmers to develop skills, training and codes of practice. "The team will gather best practices across the world, outline data challenges, identify where and how AI…

Machine learning relies on the availability of a vast amount of data for training. However, in reality, most data are scattered across different organizations and cannot be easily integrated under many legal and practical constraints. In this paper, we introduce a new technique and framework, known as federated transfer learning (FTL), to improve statistical models under a data federation. The federation allows knowledge to be shared without compromising user privacy, and enables complimentary knowledge to be transferred in the network. As a result, a target-domain party can build more flexible and powerful models by leveraging rich labels from a source-domain party. A secure transfer cross validation approach is also proposed to guard the FTL performance under the federation. The framework requires minimal modifications to the existing model structure and provides the same level of accuracy as the non-privacy-preserving approach. This framework is very flexible and can be effectively adapted to various secure multi-party machine learning tasks.