Melbourne University associate professor of computing Vanessa Teague said a "simple computer program" could be designed to detect evidence of wage underpayment using the rules laid out in the award system, but that any such project should safeguard workers' privacy by requiring informed consent. Industrial Relations Minister Christian Porter did not rule out introducing data matching as part of his wage theft crackdown and said workplace exploitation "will not be tolerated by this government". Mr Porter said the government accepted "in principle" the recommendations of the migrant worker taskforce – which included taking a "whole of government" approach and giving the Fair Work Ombudsman expanded information gathering powers. The taskforce report said inter-governmental information sharing was "an important avenue" for identifying wage under payment and could be used to "support successful prosecutions". In the latest case of alleged wage underpayment in the hospitality industry, the company behind the Crown casino eatery fronted by celebrity chef Heston Blumenthal, Dinner by Heston, this week applied to be wound up after failing to comply with a statutory notice requiring it to back pay staff for unpaid overtime.

Recent concern about harms of information technologies motivate consideration of regulatory action to forestall or constrain certain developments in the field of artificial intelligence (AI). However, definitional ambiguity hampers the possibility of conversation about this urgent topic of public concern. Legal and regulatory interventions require agreed-upon definitions, but consensus around a definition of AI has been elusive, especially in policy conversations. With an eye towards practical working definitions and a broader understanding of positions on these issues, we survey experts and review published policy documents to examine researcher and policy-maker conceptions of AI. We find that while AI researchers favor definitions of AI that emphasize technical functionality, policy-makers instead use definitions that compare systems to human thinking and behavior.

We present the "Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles" (ABOUT ML) project as an initiative to operationalize ML transparency and work towards a standard ML documentation practice. We make the case for the project's relevance and effectiveness in consolidating disparate efforts across a variety of stakeholders, as well as bringing in the perspectives of currently missing voices that will be valuable in shaping future conversations. We describe the details of the initiative and the gaps we hope this project will help address.

Stephen Chen investigates major research projects in China, a new power house of scientific and technological innovation. He has worked for the Post since 2006. He is an alumnus of Shantou University, the Hong Kong University of Science and Technology, and the Semester at Sea programme which he attended with a full scholarship from the Seawise Foundation.

In this work, learning schemes for measure-valued data are proposed, i.e. data that their structure can be more efficiently represented as probability measures instead of points on $\R^d$, employing the concept of probability barycenters as defined with respect to the Wasserstein metric. Such type of learning approaches are highly appreciated in many fields where the observational/experimental error is significant (e.g. astronomy, biology, remote sensing, etc.) or the data nature is more complex and the traditional learning algorithms are not applicable or effective to treat them (e.g. network data, interval data, high frequency records, matrix data, etc.). Under this perspective, each observation is identified by an appropriate probability measure and the proposed statistical learning schemes rely on discrimination criteria that utilize the geometric structure of the space of probability measures through core techniques from the optimal transport theory. The discussed approaches are implemented in two real world applications: (a) clustering eurozone countries according to their observed government bond yield curves and (b) classifying the areas of a satellite image to certain land uses categories which is a standard task in remote sensing. In both case studies the results are particularly interesting and meaningful while the accuracy obtained is high.

Much of the existing research on the social and ethical impact of Artificial Intelligence has been focused on defining ethical principles and guidelines surrounding Machine Learning (ML) and other Artificial Intelligence (AI) algorithms [IEEE, 2017, Jobin et al., 2019]. While this is extremely useful for helping define the appropriate social norms of AI, we believe that it is equally important to discuss both the potential and risks of ML and to inspire the community to use ML for beneficial objectives. In the present article, which is specifically aimed at ML practitioners, we thus focus more on the latter, carrying out an overview of existing high-level ethical frameworks and guidelines, but above all proposing both conceptual and practical principles and guidelines for ML research and deployment, insisting on concrete actions that can be taken by practitioners to pursue a more ethical and moral practice of ML aimed at using AI for social good.

China has developed mobile courts with artificial-intelligence judges and verdicts delivered through chat apps aiming to deal with backlogs in cases. Litigants appear by video chats as AI judge with avatar hears the cases. The country is urging digitization to streamline case-handling within its court system using cyberspace technologies such as blockchain and cloud computing, according to the Supreme People's Court in a policy paper. The paper was released in the first week of December as judicial authorities provided journalists a sneak peek of the country's first cyber court which was established in 2017 in Hangzhou city. Social media platform WeChat has reportedly handled over three million legal cases already or other judicial procedures since its launch in March.

Financial markets were cheered recently by the news that the US and China have reached a "phase one" deal to prevent further escalation of their bilateral trade war. But there is actually very little to cheer about. In exchange for China's tentative commitment to buy more US agricultural (and some other) goods, and modest concessions on intellectual property rights and the yuan, the US agreed to withhold tariffs on another $160bn (£124bn) worth of Chinese exports, and to roll back some of the tariffs introduced on 1 September. The good news for investors is that the deal averted a new round of tariffs that could have tipped the US and the global economy into recession and crashed global stock markets. The bad news is that it represents just another temporary truce amid a much larger strategic rivalry encompassing trade, technology, investment, currency and geopolitical issues.

Courts in China are increasingly relying on blockchain technology and artificial intelligence to resolve legal issues through "smart internet courts," where witnesses communicate with virtual, AI-enabled judges. According to a Cointelegraph report, Xinhua state news agency revealed that over 3.1 million cases in the country were settled using the technologies in smart internet courts from March to October 2019. Xinhua reported that the smart courts are made up of virtual judges enabled by AI technology that can communicate with citizens through multiple screens. The first-ever smart court in China was introduced in 2017 in Hangzhou city, before the Chinese government expanded these courts to Beijing and Guangzhou. Zhang Wen, President of the Beijing Internet Court, has stated that these new courts have adopted both AI and blockchain technology to resolve legal issues.

Contract analytics are now a staple of the legal industry. Driven by continuing advances in artificial intelligence ("AI"), contract analytics has changed the way the legal industry thinks and works. It is no longer a question of when the disruption will arrive, but when the legal industry will reach a "tipping point" of growth and development. This will naturally result from multiple legal-tech advances, driven by contract analytics, as well as related technologies, that exert pressure on how legal pros work, which are coming together to create a bigger cumulative effect. While much of this is hard to predict, the tipping point is coming into sharper focus every day.