The spectacular expansion of the Internet led to the development of a new research problem in the natural language processing field: automatic toxic comment detection, since many countries prohibit hate speech in public media. There is no clear and formal definition of hate, offensive, toxic and abusive speeches. In this article, we put all these terms under the "umbrella" of toxic speech. The contribution of this paper is the design of binary classification and regression-based approaches aiming to predict whether a comment is toxic or not. We compare different unsupervised word representations and different DNN classifiers. Moreover, we study the robustness of the proposed approaches to adversarial attacks by adding one (healthy or toxic) word. We evaluate the proposed methodology on the English Wikipedia Detox corpus. Our experiments show that using BERT fine-tuning outperforms feature-based BERT, Mikolov's word embedding or fastText representations with different DNN classifiers.

Certain research strands can yield "forbidden knowledge". This term refers to knowledge that is considered too sensitive, dangerous or taboo to be produced or shared. Discourses about such publication restrictions are already entrenched in scientific fields like IT security, synthetic biology or nuclear physics research. This paper makes the case for transferring this discourse to machine learning research. Some machine learning applications can very easily be misused and unfold harmful consequences, for instance with regard to generative video or text synthesis, personality analysis, behavior manipulation, software vulnerability detection and the like. Up to now, the machine learning research community embraces the idea of open access. However, this is opposed to precautionary efforts to prevent the malicious use of machine learning applications. Information about or from such applications may, if improperly disclosed, cause harm to people, organizations or whole societies. Hence, the goal of this work is to outline norms that can help to decide whether and when the dissemination of such information should be prevented. It proposes review parameters for the machine learning community to establish an ethical framework on how to deal with forbidden knowledge and dual-use applications.

Artificial intelligence (AI) has quickly emerged as the defining technology of our society. Gartner forecasts that by 2020, AI will be a top-five investment priority for more than 30% of CIOs. UK tech companies have secured £1 billion deal investment from the UK government which will support their research and development. Though the discussion of adopting AI is still very compelling for businesses and consumers alike, there are still legitimate concerns, as raised by The Guardian's Inequality Project: "When the data we feed the machines reflects the history of our own unequal society, we are in effect asking the program to learn our own biases." It has, therefore, become paramount that CIOs know some uses of AI that could cause problems – the bad, the biased and the unethical – and what they can do to make sure their business remains on the right side.

At Legalweek New York 2019, every vendor seemed to tout their product's artificial intelligence (AI). AI, sometimes called machine intelligence, is the intelligence demonstrated by computers in contrast to natural human intelligence. We have all experienced AI in modern life: Netflix recommendations, Amazon's and Spotify's suggestions, and LinkedIn's and Facebook's prods. Who hasn't heard an IBM Watson ad? These may be recent, but AI has been around the legal world for a long while, in both legal research and electronic discovery.

When society uses artificial intelligence (AI) to help build judgments about individuals, fairness and equity are critical considerations. On Nov. 12, Brookings Fellow Nicol Turner-Lee sat down with Solon Barocas of Cornell University, Natasha Duarte of the Center for Democracy & Technology, and Karl Ricanek of the University of North Carolina Wilmington to discuss artificial intelligence in the context of societal bias, technological testing, and the legal system. Artificial intelligence is an element of many everyday services and applications, including electronic devices, online search engines, and social media platforms. In most cases, AI provides positive utility for consumers--such as when machines automatically detect credit card fraud or help doctors assess health care risks. However, there is a smaller percentage of cases, such as when AI helps inform decisions on credit limits or mortgage lending, where technology has a higher potential to augment historical biases.

If you're worried about how facial recognition technology is being used, you should be. And things are about to get a lot scarier unless new regulation is put in place. Already, this technology is being used in many U.S. cities and around the world. Rights groups have raised alarm about its use to monitor public spaces and protests, to track and profile minorities, and to flag suspects in criminal investigations. The screening of travelers, concertgoers and sports fans with the technology has also sparked privacy and civil liberties concerns.

On 11 September 2019, the Committee of Ministers of the Council of Europe set up an Ad Hoc Committee on Artificial Intelligence – CAHAI. The Committee will examine the feasibility and potential elements on the basis of broad multi-stakeholder consultations, of a legal framework for the development, design and application of artificial intelligence, based on Council of Europe's standards on human rights, democracy and the rule of law.

The General Data Protection Regulation (GDPR) came into force in May 2018, to unify and regulate how data is processed, used, stored and exchanged for citizens and residents within the European Union (EU). While this law has been in effect for some time now, it still raises multiple questions for businesses around the world. This is especially true for both those who provide and those who leverage Artificial Intelligence (AI) while conducting business in the EU. AI is dependent upon a healthy flow of data in order to drive business growth and generate valuable business insights. Article 22 of the GDPR concerns automated profiling and decision making and outlines the ramifications for the incorrect use of data in these circumstances.

Technological advancements have made a profound impact in all aspect of our lives. And as a result, many businesses are pushing forward with their digital agendas. Digitisation has made its way into the legal system, too. The UK government published its policy paper in 2017, setting out how to develop a world-leading digital economy that works for everyone. For both the government and law firms, this change is mostly driven by client pressure, according to The Law Society.

Can a credit card be sexist? It's not a question most people would have thought about before this week, but on Monday, state regulators in New York announced an investigation into claims of gender discrimination by Apple Card. The algorithms Apple Card used to set credit limits are, it has been reported, inherently biased against women. Tech entrepreneur David Heinemeier Hansson (@DHH) claimed that the card offered him 20 times more credit than his wife, even though she had the better credit score, while Apple's own co-founder Steve Wozniak went to Twitter with a similar story, despite he and his wife sharing bank accounts and assets. Goldman Sachs, the New York bank that backs the Apple Card, released a statement rejecting this assertion, saying that when it comes to assessing credit, they "have not and will not make decisions based on gender."