From self-driving vehicles and back-flipping robots to virtual assistants who book our next appointment at the hair salon or at that restaurant for dinner - machine learning systems are becoming increasingly ubiquitous. The main reason for this is that these methods boast remarkable predictive capabilities. However, most of these models remain black boxes, meaning that it is very challenging for humans to follow and understand their intricate inner workings. Consequently, interpretability has suffered under this ever-increasing complexity of machine learning models. Especially with regards to new regulations, such as the General Data Protection Regulation (GDPR), the necessity for plausibility and verifiability of predictions made by these black boxes is indispensable. Driven by the needs of industry and practice, the research community has recognised this interpretability problem and focussed on developing a growing number of so-called explanation methods over the past few years. These methods explain individual predictions made by black box machine learning models and help to recover some of the lost interpretability. With the proliferation of these explanation methods, it is, however, often unclear, which explanation method offers a higher explanation quality, or is generally better-suited for the situation at hand. In this thesis, we thus propose an axiomatic framework, which allows comparing the quality of different explanation methods amongst each other. Through experimental validation, we find that the developed framework is useful to assess the explanation quality of different explanation methods and reach conclusions that are consistent with independent research.

The plaintiffs, who include Tinder co-founders Sean Rad, Justin Mateen and Jonathan Badeen, allege that IAC and Match repeatedly falsified financial information to lowball the app's valuation. That prevented bigger payouts to the co-founders and executives, the suit says.

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Climate change has been bleaching coral reefs, decimating the local marine species that call them home, since at least the first major observations were recorded in the Caribbean in 1980. Thankfully, new A.I. cataloguing designed to identify the geographic regions where coral is still thriving hopes to reverse the trend, saving some of the world's most dense and varied aquatic ecosystems from all-but-certain extinction. There are numerous reasons why we need to care about saving coral reefs, from the ethical to the economic. In addition to housing about a quarter of marine species, these reefs provide $375 billion USD in revenue to the world economy, according to the Guardian, and food security to half a billion people. Without them, researchers say countless species and the entire ocean fishing industry that depends on them would simply evaporate.

Technology has a place in every industry, and today, 'every company is a technology company'. In the first of this three part series on AI in legal industry, we looked at what the stage of AI adoption in law, and how it is – if at all – creating a more strategic role for associates in law firms. The article established – with the help of Geoffrey Vance, the chair of Perkins Coie's E-Discovery Services and Strategy Practice, and Alvin Lindsay, partner at Hogan Lovells – that AI adoption was at a fairly early stage, although its use is not a new phenomenon. It also became clear that, as suggested by a report from ALM Intelligence, that AI will be crucial in helping law firms overcome challenges and survive moving forward. The second part of this series will look at AI's broader role within the legal industry, with specific case studies from Perkins Coie and Hogan Lovells.

We live in an era where there's perhaps more focus than ever before on how an organization treats its employees. Stories about gender-based pay gaps, lack of diversity, and sexual harassment are front-page news around the world. The ensuing outrage from stockholders and the public have pummeled share prices and reputations, with more than a few top executives going from C-suite to unemployment line as a result. If there were ever a time when HR leaders needed to be more actively involved at the highest levels of the enterprise, it's now. The value that a great HR executive can bring to an organization is enormous, from preventing that loss of reputation to boosting worker engagement and productivity, to being the moral compass of an organization.

One year ago, we wrote about the world's first robot lawyer. It is a website with a chatbot that started off with a single and free legal service: helping to appeal unfair parking tickets. When the article was published, the services was available in the UK, and in New York and Seattle. At the time, it had helped overturn traffic tickets to the value of 4 million dollars. Apart from appealing parking tickets, the website could already assist you, too, in claiming compensation if your flight was delayed.

As artificial intelligence continues to evolve, it is having profound impact on a range of sectors seemingly unrelated to it, such as international relations. Some countries are pursuing AI more or less within the confines of international law and generally accepted principles of doing business, while others are choosing to do what is necessary to attempt to achieve AI supremacy outside those boundaries. In the process, AI is slowly altering the balance of power between global actors and among alliances in a number of ways. Just as becoming adept in the cyber arena levels the playing field – giving countries such as Iran and North Korea the ability to go head to head with China, Russia and that US in cyber space – the pursuit of AI supremacy is providing an increased competitive edge in international business to some smaller, otherwise less competitive nations, enhancing their ability to secure preferential trade and investment arrangements with other countries, raising their global profile, and enabling them to progress into previously unimagined areas of international trade, investment, and diplomacy. How AI is deployed by governments can have serious consequences in international relations, particularly if a given government has unusual capabilities in the AI arena.

This paper provides a link between causal inference and machine learning techniques - specifically, Classification and Regression Trees (CART) - in observational studies where the receipt of the treatment is not randomized, but the assignment to the treatment can be assumed to be randomized (irregular assignment mechanism). The paper contributes to the growing applied machine learning literature on causal inference, by proposing a modified version of the Causal Tree (CT) algorithm to draw causal inference from an irregular assignment mechanism. The proposed method is developed by merging the CT approach with the instrumental variable framework to causal inference, hence the name Causal Tree with Instrumental Variable (CT-IV). As compared to CT, the main strength of CT-IV is that it can deal more efficiently with the heterogeneity of causal effects, as demonstrated by a series of numerical results obtained on synthetic data. Then, the proposed algorithm is used to evaluate a public policy implemented by the Tuscan Regional Administration (Italy), which aimed at easing the access to credit for small firms. In this context, CT-IV breaks fresh ground for target-based policies, identifying interesting heterogeneous causal effects.

Over 200 attendees attended the blockchain meetup in Istanbul, Turkey co-organized by SamuraiSignals and MATRIX AI Network on July 28th, to explore leading edge developments in blockchain. In his talk, IDA cofounder, Mr. Walter Wang outlined four innovations shaping the future of asset digitization. Wang said that the technical basis for asset digitization, is creating a digitized representation of real assets or IP assets in a binary code format -- however, there are currently the following challenges to overcome before the technology can be applied widely. There is an asymmetry in transparency of asset information. How to extend legal protections to digital assets?