Hikvision has marketed an AI camera that automatically identifies Uyghurs, on its China website, only covering it up days ago after IPVM questioned them on it. This AI technology allows the PRC to automatically track Uyghur people, one of the world's most persecuted minorities. Hikvision's product description states this camera supports Uyghur recognition (screenshot via Google Translate): Capable of analysis on target personnel's sex (male, female), ethnicity (such as Uyghurs, Han) and color of skin (such as white, yellow, or black), whether the target person wears glasses, masks, caps, or whether he has beard, with an accuracy rate of no less than 90%. By April 2019, Hikvision was well-aware of the human rights issues surrounding Xinjiang; that same month, they disclosed in their ESG report that they had "recently commissioned an internal review" on the matter. The PRC officially recognizes 56 ethnic groups, which the Chinese ambassador recently described as being'part of big family of Chinese nation'.

This paper presents our experience on building RDF knowledge graphs for an industrial use case in the legal domain. The information contained in legal information systems are often accessed through simple keyword interfaces and presented as a simple list of hits. In order to improve search accuracy one may avail of knowledge graphs, where the semantics of the data can be made explicit. Significant research effort has been invested in the area of building knowledge graphs from semi-structured text documents, such as XML, with the prevailing approach being the use of mapping languages. In this paper, we present a semantic model for representing legal documents together with an industrial use case. We also present a set of use case requirements based on the proposed semantic model, which are used to compare and discuss the use of state-of-the-art mapping languages for building knowledge graphs for legal data. Keywords: Mapping languages · Legal Knowledge Graphs · Legal semantic model 1 Introduction The body of law to which citizens and businesses have to adhere is constantly increasing in volume and complexity [2]. The information contained in such a body of law is usually provided by unstructured text within legal documents, for which a number of systems have been developed. The information made available by such legal information systems, however, is often accessed with simple, keyword-based search interfaces and presented as a simple list of hits [7].

--Fair classification has become an important topic in machine learning research. While most bias mitigation strategies focus on neural networks, we noticed a lack of work on fair classifiers based on decision trees even though they have proven very efficient. In an up-to-date comparison of state-of- the-art classification algorithms in tabular data, tree boosting outperforms deep learning [1]. For this reason, we have developed a novel approach of adversarial gradient tree boosting. The objective of the algorithm is to predict the output Y with gradient tree boosting while minimizing the ability of an adversarial neural network to predict the sensitive attribute S . The approach incorporates at each iteration the gradient of the neural network directly in the gradient tree boosting. We empirically assess our approach on 4 popular data sets and compare against state-of- the-art algorithms. The results show that our algorithm achieves a higher accuracy while obtaining the same level of fairness, as measured using a set of different common fairness definitions. I NTRODUCTION Machine learning models are increasingly used in decision making processes. In many fields of application, they generally deliver superior performance compared with conventional, deterministic algorithms. However, those models are mostly black boxes which are hard, if not impossible, to interpret.

Internet censorship, basically, is a very effective strategy used by dictatorial governments to limit access to information available online for controlling freedom of expression and prevent rebellion and discord. Countries at the forefront of adopting Internet censorship, as per the findings of the 2019 Freedom House report, are India and China as these are declared to be the worst abusers of digital freedom. Conversely, the US, Brazil, Sudan, and Kazakhstan are the countries where Internet freedom has considerably declined recently. When a country curbs Internet freedom, activists need to find ways to evade it. However, they may not need to manually search for it now that "Geneva" is here. The term is a shorter version of Genetic Evasion.

Google became what it is by creating advanced new technology and throwing it open to all. Giant businesses and individuals alike can use the company's search and email services, or tap its targeting algorithms and vast audience for ad campaigns. Yet Google's progress on artificial intelligence now appears to have the company rethinking its do-what-you-will approach. The company has begun withholding or restricting some of its AI research and services, to protect the public from misuse. Google CEO Sundar Pichai has made "AI first" a company slogan, but the company's wariness of AI's power has sometimes let its competitors lead instead.

The possibility of man-made machines turning against their creators has become a trendy topic these days. Undoubtedly, Isaac Asimov's Three Laws of Robotics are no longer fit for purpose. For the sake of the global public good, we need something serious and more specific to safeguard our limitless ambitions - and humanity itself. Today, the internet connects more than half the world's population. And although the internet provides us with convenience and efficiency, it also brings threats. This is especially true in an age in which a good deal of our daily life is driven by big data and artificial intelligence.

Catalyst president and CEO Lorraine Hariton, who works with Fortune 500 companies to eliminate bias in their technology and systems, gives her thoughts on the controversy surrounding gender and the new Apple Card's algorithm. She says artificial intelligence can become biased if leaders and teams aren't diverse and inclusive. The Apple Card gender bias allegation is a lesson for Silicon Valley, which has suffered from sexism issues for a long time, according to one tech expert. Apple made headlines Sunday when the artificial intelligence algorithm behind its new Apple Card, in partnership with Goldman Sachs, was accused of gender discrimination after Apple co-founder Steve Wozniak and another male tech entrepreneur said they got much higher lines of credit for their card applications than their wives did. Catalyst president and CEO Lorraine Hariton, who works with Fortune 500 companies to eliminate bias in technology and systems, joined FOX Business' Liz Claman on Friday and said she was not surprised by the accusation.

Intelligent robots, intelligent virtual assistants, intelligent cars intelligently driving themselves, intelligent search systems learning and already knowing our browsing habits, interests, knowing what we are going to do online and even in real life. Siri and Alexa, Tesla, Amazon and Google, artificially intelligent algorithms that are everywhere, able to do many things instead of us. In the future, AI is going to change everything. As for now, there are lots of discussions about 4 main AI trends that are prone to shape the AI mechanized future of mankind. Here they are: deep learning, facial recognition, cloud, privacy and policy.

Advances in computer vision have brought us to the point where we have the ability to synthesise realistic fake content. Such approaches are seen as a source of disinformation and mistrust, and pose serious concerns to governments around the world. Convolutional Neural Networks (CNNs) demonstrate encouraging results when detecting fake images that arise from the specific type of manipulation they are trained on. However, this success has not transitioned to unseen manipulation types, resulting in a significant gap in the line-of-defense. We propose a Hierarchical Memory Network (HMN) architecture, which is able to successfully detect faked faces by utilising knowledge stored in neural memories as well as visual cues to reason about the perceived face and anticipate its future semantic embeddings. This renders a generalisable face tampering detection framework. Experimental results demonstrate the proposed approach achieves superior performance for fake and fraudulent face detection compared to the state-of-the-art.

Flinders University has launched a range of new law courses studyng the legal implications of emerging technologies and how they can be used to in legal practice to increase the quality of service. It follows a successful pilot at the uni in which law students designed and built their own apps using Neota Logic's artificial intelligence software to increase their technological literacy. The app building pilot was also billed as a way of increasing people's access to legal services in areas where they might otherwise not be able to - another goal of the new suite of courses. Dean of Law, Associate Professor Tania Leiman said in a statement that the new courses will be available for study from March next year. "On of the core topics is Law in a'Digital Age', which seeks to equip students with the digital skills to assist clients to access justice," Leiman said.