Joe Toscano is an award-winning designer and former consultant for Google who left in 2017 due to ethical concerns. Upgrade your inbox and get our editors' picks twice a month. Privacy is a fundamental human right that has become one of the most illusive and least understood topics of the Internet. However, the time is coming for change, and it's up to us whether that's going to happen willfully or through regulation. This article will explain exactly why making these changes is so critical to the success of your business and how you can make the changes that need to be made in a way that also positively impacts your bottom line. Privacy is a fundamental human right that allows us to be our true selves. It's what allows us to be weirdos without shame. It allows us to have dissenting opinions without consequence.

Two Artificial Intelligence-driven Internet paradigms may emerge in the near future. One will be based on logic, smart enterprises and human merit while the other may morph into an Orwellian control tool. Even former Google CEO Eric Schmidt has foreseen a bifurcation of the Internet by 2028 and China's eventual triumph in the AI race by 2030. In the meantime, the US seems more interested in deflecting the smart questions of today than in building the smart factories of tomorrow. Nothing embodies this better than the recent attempt by MIT's Computer Science and Artificial Intelligence Lab (CSAIL) and the Qatar Computing Research Institute (QCRI) to create an AI-based filter to "stamp out fake-news outlets before the stories spread too widely."

New technical artifacts connected to the Internet constantly share, process, and storage a huge amount of data. This practice is what unifies the concept of Internet of Things ("IoT") to the concept of Big Data. With the growing dissemination of Big Data and computing techniques, technological evolution and economic pressure spread rapidly, and algorithms have become a great resource for innovation and business models. This rapid diffusion of algorithms and their increasing influence, however, have consequences for the market and for society, consequences which include questions of ethics and governance. Automated systems that turn on the lights and warm the dinner by realizing that you're returning home from work, smart bracelets and insoles that share with your friends how much you've walked or cycled during the day in the city or sensors that automatically warn farmers when an animal is sick or pregnant.

We've reached a point where human (cognitive) task performance is being leveraged or even replaced by AI. So who or what is responsible for what this AI does? While the question seems simple enough, legal answers from the field are apparently opaque and embroiled. This is caused by the fact that AI is performing human-like tasks without having the clear legal accountability of one, and the question is whether it should have any. Fortunately, now that machine learning and artificial intelligence are protruding on an ever-increasing amount of practical domains, real-world legal interpretations and guiding principles are forming around the topic.

Artificial intelligence is shaking-up the legal industry. This includes some breakthrough AI technology from Thomson Reuters, which will affect how attorneys work with their clients. Examples include changing billable hours, competitive advantages to building cases, and so on. To find out more about the new AI technology and to understand how new technologies are disrupting law in general, Digital Journal caught up with Dr. Khalid Al-Kofahi from Thomson Reuters. Digital Journal: How has artificial intelligence advanced in the legal space in recent years?

Deep learning methods are often difficult to apply in the legal domain due to the large amount of labeled data required by deep learning methods. A recent new trend in the deep learning community is the application of multi-task models that enable single deep neural networks to perform more than one task at the same time, for example classification and translation tasks. These powerful novel models are capable of transferring knowledge among different tasks or training sets and therefore could open up the legal domain for many deep learning applications. In this paper, we investigate the transfer learning capabilities of such a multi-task model on a classification task on the publicly available Kaggle toxic comment dataset for classifying illegal comments and we can report promising results.

We observe a rapid increase in machine learning models for learning data representations that remove the semantics of protected characteristics, and are therefore able to mitigate unfair prediction outcomes. This is indeed a positive proliferation. All available models however learn latent embeddings, therefore the produced representations do not have the semantic meaning of the input. Our aim here is to learn fair representations that are directly interpretable in the original input domain. We cast this problem as a data-to-data translation; to learn a mapping from data in a source domain to a target domain such that data in the target domain enforces fairness definitions, such as statistical parity or equality of opportunity. Unavailability of fair data in the target domain is the crux of the problem. This paper provides the first approach to learn a highly unconstrained mapping from source to target by maximizing (conditional) dependence of residuals - the difference between data and its translated version - and protected characteristics. The usage of residual statistics ensures that our generated fair data should only be an adjustment of the input data, and this adjustment should reveal the main difference between protected characteristic groups. When applied to CelebA face image dataset with gender as protected characteristic, our model enforces equality of opportunity by adjusting eyes and lips regions. In Adult income dataset, also with gender as protected characteristic, our model achieves equality of opportunity by, among others, obfuscating wife and husband relationship. Visualizing those systematic changes will allow us to scrutinize the interplay of fairness criterion, chosen protected characteristics, and the prediction performance.

In the legal domain it is important to differentiate between words in general, and afterwards to link the occurrences of the same entities. The topic to solve these challenges is called Named-Entity Linking (NEL). Current supervised neural networks designed for NEL use publicly available datasets for training and testing. However, this paper focuses especially on the aspect of applying transfer learning approach using networks trained for NEL to legal documents. Experiments show consistent improvement in the legal datasets that were created from the European Union law in the scope of this research. Using transfer learning approach, we reached F1-score of 98.90\% and 98.01\% on the legal small and large test dataset.

Europe's new privacy rules risk becoming outdated less than five months after being put in place, the chairman of Nokia warned today as he urged policymakers to update legislation as more companies invest in artificial intelligence (AI) technology. Speaking at an AI event in Finland, Risto Siilasmaa warned that the EU's General Data Protection Regulation (GDPR) was "largely designed before anyone in Brussels had heard the term machine learning". "We need to regularly update the rules and make sure they are cutting-edge and respond to these new needs," he said. "I was talking to a large number of commissioners and director generals about machine learning last autumn, and I gave them a lesson in...

Amazon yesterday filed a patent with the U.S. Patent and Trademark Office related to detecting physical and emotional wellbeing of users based on interactions captured in voice data. The first example in the patent application depicts a user coughing while asking Alexa about being hungry. Alexa responds by suggesting a chicken soup recipe and when refused then offers to order cough drops with one-hour delivery. The voice recognition system is using sounds such as a cough or sniffle to determine if a user is unwell. However, the patent is not limited by these sounds and could be extended to different types of normal speech.