When it comes to drone regulations, the FAA's rules trump anything local governments conjure up. That's what a federal court in Massachusetts has proven when it ruled in favor of a commercial drone owner who sued the city of Newton over its drone ordinance. Newton resident Michael Singer filed the lawsuit in a bid to eliminate some of the city's rules that don't align with the FAA's, including having to register with every municipality it has to fly over and to maintain an altitude of 400 feet and above over private and Newton city property. Two of the rules he chose not to challenge prohibit operating drones in a reckless manner and the use of drones to spy on people. Singer argued that having to register with every municipality would make flights impossible, since an unmanned flying vehicle could cross several for a trip that takes a few minutes.

Safety Principle: AI systems should be safe and secure throughout their operational lifetime and verifiably so where applicable and feasible. When a new car is introduced to the world, it must pass various safety tests to satisfy not just government regulations, but also public expectations. In fact, safety has become a top selling point among car buyers. Whatever the latest generation of any technology happens to be -- from appliances to airplanes -- manufacturers know that customers expect their products to be safe from start to finish. Artificial intelligence is no different.

At the low end, we will surely see the rise of self-service platforms, perhaps in some cases tapping directly into flows of client data for accounting and audit, and this will open up a lot of competitive innovation as clients realise they can shape the offering provided by professionals, rather than just accept the customary service. But at the higher end, there is a good chance that small teams of entrepreneurial professionals who know how to get the most out of their AI systems will be able to handle large and complex transactions or matters for their clients. Whilst it is reasonably straightforward to think of positive futures for professionals, the question of what this means for the firm is a slightly different one. Where firms can aggregate analytical power and develop a platform to extend their value proposition both internally and externally, or develop compelling brands and service delivery models that clients trust, then there remains a strong case to be made for their continued existence. But it is unlikely that they will continue with the current model or way of working, which does not add sufficient value to the work of individual professionals, except perhaps in the most complex of matters where co-ordination of various internal disciplines is needed.

When Apple's new iPhone X arrives next month, its Face ID technology will introduce a new era of convenience--but also new risks of broad face-based surveillance by corporations and governments. Apple's strong record on privacy means it's likely to deploy the facial recognition tool responsibly, but that doesn't account for third-party companies that plan to integrate Face ID into their apps. Such companies could seek to assemble their own databases of faces and, in the worst case scenario, use a facial database to identify consumers online and in the streets for ad purposes. Apple has yet to disclose full details of how Face ID will operate, though a source familiar with the tool says there is a plan to prevent app makers from violating user privacy. Meanwhile, outside of a single state law, consumers will have little recourse if companies begin to collect images of their face without consent.

London could lose all of its Ubers, courtesy of the city's transportation agency. On Friday, Transport for London announced it would not renew the ridehailing giant's license to operate in the city, citing the company's "lack of corporate responsibility." The license expires September 30, though, unsurprisingly, Uber has declared it will exercise its right to an appeal. The company is able to continue operating in the city as long as the legal process drags on, but it didn't wait for its lawyers to prepare their case before dusting off the weapon that has carried it through many a battle: public fervor. Right after TfL dropped its news, Uber posted a petition on Change.org.

For months I have regularly tweeted a response to my colleagues in the AI Safety community about being a supporter of independent audit, but recently it has become clear to me that I have insufficiently explained how this works and why it is so powerful. This blog will attempt to do that. Unfortunately it is likely be longer than my typical blog, so apologies in advance. ForHumanity, or similar entity, that exists, not for profit, but for the benefit of humanity would conduct detailed, transparent and iterative audits on all developers of AI. The criteria that is analysed is important, but it is not the most important aspect of independent audit.

If you are a data scientist, a software developer, or in the social and human sciences with interest in digital humanities, then you're no stranger to the ongoing discussions on how algorithms embed biases, and discrimination and the call for critical and ethical engagement. I have keenly been following such discussion for a while and this post is an attempt to put together the articles, books, book reviews, videos, interviews, twitter threads and so on., that I've come across in one place so it can be used as a resource. This list is by no means exhaustive and as more and more awareness is being raised, there are more pieces/articles/journal papers being written on a daily basis. I plan to update these lists regularly. Also, if you think there are relevant material that I have not included, please leave them as a comment and I will add them.

How can you do good with data? The ethical and legal principles surrounding data and its use--from information to analytics and insight and beyond, into data science and artificial intelligence (AI)--are global in nature, even if the laws are not. And, regardless of whether your company is local or has offices around the world, if you're using data (and you probably are), it's important to know how to properly handle it, what to consider, and how to achieve good with it. In short, data professionals today need both the frameworks and the methods in their job to achieve optimal results while being good stewards of their critical role in society today. Corporations, governments, and individuals have powerful tools in Analytics and AI to create real-world outcomes.

Content-dense news report important factual information about an event in direct, succinct manner. Information seeking applications such as information extraction, question answering and summarization normally assume all text they deal with is content-dense. Here we empirically test this assumption on news articles from the business, U.S. international relations, sports and science journalism domains. Our findings clearly indicate that about half of the news texts in our study are in fact not content-dense and motivate the development of a supervised content-density detector. We heuristically label a large training corpus for the task and train a two-layer classifying model based on lexical and unlexicalized syntactic features. On manually annotated data, we compare the performance of domain-specific classifiers, trained on data only from a given news domain and a general classifier in which data from all four domains is pooled together. Our annotation and prediction experiments demonstrate that the concept of content density varies depending on the domain and that naive annotators provide judgement biased toward the stereotypical domain label. Domain-specific classifiers are more accurate for domains in which content-dense texts are typically fewer. Domain independent classifiers reproduce better naive crowdsourced judgements. Classification prediction is high across all conditions, around 80%.