De-identification of electronic health records (EHR) is a vital step towards advancing health informatics research and maximising the use of available data. It is a two-step process where step one is the identification of protected health information (PHI), and step two is replacing such PHI with surrogates. Despite the recent advances in automatic de-identification of EHR, significant obstacles remain if the abundant health data available are to be used to the full potential. Accuracy in de-identification could be considered a necessary, but not sufficient condition for the use of EHR without individual patient consent. We present here a comprehensive review of the progress to date, both the impressive successes in achieving high accuracy and the significant risks and challenges that remain. To best of our knowledge, this is the first paper to present a complete picture of end-to-end automatic de-identification. We review 18 recently published automatic de-identification systems -designed to de-identify EHR in the form of free text- to show the advancements made in improving the overall accuracy of the system, and in identifying individual PHI. We argue that despite the improvements in accuracy there remain challenges in surrogate generation and replacements of identified PHIs, and the risks posed to patient protection and privacy.

The distribution of health care payments to insurance plans has substantial consequences for social policy. Risk adjustment formulas predict spending in health insurance markets in order to provide fair benefits and health care coverage for all enrollees, regardless of their health status. Unfortunately, current risk adjustment formulas are known to undercompensate payments to health insurers for specific groups of enrollees (by underpredicting their spending). Much of the existing algorithmic fairness literature for group fairness to date has focused on classifiers and binary outcomes. To improve risk adjustment formulas for undercompensated groups, we expand on concepts from the statistics, computer science, and health economics literature to develop new fair regression methods for continuous outcomes by building fairness considerations directly into the objective function. We additionally propose a novel measure of fairness while asserting that a suite of metrics is necessary in order to evaluate risk adjustment formulas more fully. Our data application using the IBM MarketScan Research Databases and simulation studies demonstrate that these new fair regression methods may lead to massive improvements in group fairness with only small reductions in overall fit.

Machine Learning (ML) and Deep Learning (DL) models have achieved state-of-the-art performance on multiple learning tasks, from vision to natural language modelling. With the growing adoption of ML and DL to many areas of computer science, recent research has also started focusing on the security properties of these models. There has been a lot of work undertaken to understand if (deep) neural network architectures are resilient to black-box adversarial attacks which craft perturbed input samples that fool the classifier without knowing the architecture used. Recent work has also focused on the transferability of adversarial attacks and found that adversarial attacks are generally easily transferable between models, datasets, and techniques. However, such attacks and their analysis have not been covered from the perspective of unsupervised machine learning algorithms. In this paper, we seek to bridge this gap through multiple contributions. We first provide a strong (iterative) black-box adversarial attack that can craft adversarial samples which will be incorrectly clustered irrespective of the choice of clustering algorithm. We choose 4 prominent clustering algorithms, and a real-world dataset to show the working of the proposed adversarial algorithm. Using these clustering algorithms we also carry out a simple study of cross-technique adversarial attack transferability.

In countries around the world, natural disasters have been much in the news. If you had a hunch such calamities were increasing, you're right. In 2017, hurricanes, earthquakes, and wildfires cost $306 billion worldwide, nearly double 2016's losses of $188 billion. Natural disasters caused by climate change, extreme weather, and aging and poorly designed infrastructure, among other risks, represent a significant risk to human life and communities. Globally, $94 trillion in new investment is needed to keep pace with population growth, with a large portion of that going toward repair of the built environment.

DAVOS, SWITZERLAND – The star who stole the stage at the annual meeting of the World Economic Forum, which just ended here yesterday, wasn't the stuff of flesh and blood but of data-driven algorithms. US President Donald Trump, China's Xi Jinping, India's Narendra Modi, France's Emmanuel Macron and Great Britain's Theresa May were no shows at this gathering of global movers and shakers, occupied with more pressing matters at home. That left hundreds of global business executives with less distraction as they turned their attention to Artificial Intelligence (AI), a term few of them knew even a couple of years ago and a technology they still don't fully comprehend. Yet in one session after another, they shared what they were (or weren't) doing about it and learned how AI would transform their industries, their societies and international relations, perhaps as no technology before it. Not even news late in the week from Venezuela shifted the conversation all that much.

Peggy Smedley talks about how the law is going to play an important role in shaping the cybersecurity landscape going forward. She explains that the lack of awareness about cybersecurity is a real problem in our industry--pointing to one statistic that shows 86% of IT and security decisionmakers say their organizations need to improve their awareness of IoT threats. She recommends IT leaders focus on becoming more aware of cybersecurity. Peggy Smedley dives into the topic of cognitive technologies and how they impact the IoT (Internet of Things). She discusses recent research from Deloitte, which shows roughly 76% of respondents say they expect cognitive technologies to transform their companies in just three years or less, and she explains the business value of cognitive technologies goes beyond cutting costs.

This week at the World Economic Forum (WEF), an annual gathering that put tech executives at the same table as far-right Brazilian president Jair Bolsonaro, Salesforce CEO Marc Benioff called San Francisco the canary in the coal mine. "San Francisco is kind of a train wreck; we have a real inequality problem," he said. Benioff and Salesforce, which has the largest skyscraper on the San Francisco skyline, led the Prop C campaign, a $300 million business tax aimed at reducing homelessness in San Francisco that is currently held up in court. Benioff also asserted at the gathering in Davos, Switzerland that artificial intelligence is "a new human right" that all people deserve. "Those who have the artificial intelligence will be smarter, will be healthier, will be richer, and of course, you've seen their warfare will be significantly more advanced," he said.

ESA astronaut Alexander Gerst welcomed a new face to the Columbus laboratory, thanks to the successful commissioning of technology demonstration Cimon. Short for Crew Interactive Mobile CompanioN, Cimon is a 3D-printed plastic sphere designed to test human-machine interaction in space. Developed and built by Airbus in Friedrichshafen and Bremen, Germany, on behalf of German Space Agency DLR, Cimon uses artificial intelligence software by IBM Watson. Its scientific aspects are overseen by researchers at Ludwig Maximilians University Clinic in Munich. This video shows Alexander's first interactions with Cimon on board the International Space Station.

Posted on January 15th, 2019 by Dr. Francis Collins Thanks to advances in wearable health technologies, it's now possible for people to monitor their heart rhythms at home for days, weeks, or even months via wireless electrocardiogram (EKG) patches. In fact, my Apple Watch makes it possible to record a real-time EKG whenever I want. For true medical benefit, however, the challenge lies in analyzing the vast amounts of data--often hundreds of hours worth per person--to distinguish reliably between harmless rhythm irregularities and potentially life-threatening problems. Now, NIH-funded researchers have found that artificial intelligence (AI) can help. A powerful computer "studied" more than 90,000 EKG recordings, from which it "learned" to recognize patterns, form rules, and apply them accurately to future EKG readings.

Anyone who uses Facebook can't have failed to notice that in recent years it has become rather good at recognising faces – it is as if the social network has been scrolling through photos of your friends for years and is now as familiar with them as you are. You may find this unsettling or remarkable, but the reality is that this is only the most visible manifestation of the rapid improvements in artificial intelligence under way across the globe – improvements that have, for example, led the US government to announce it is trialling facial recognition systems as a security measure at the White House, and sparked protests – on ethical and privacy grounds – against e-commerce giant Amazon for selling such technology. "The exciting part is that the results produced are free from bias, which improves confidence and relationships between clients and contractors" The artificial intelligence (AI) technologies behind facial recognition are machine learning, in which computers use algorithms to analyse data and learn without assistance, and deep learning – a similar but more advanced system based on "recurrent neural networks" – that replicate some of the ways the brain works, for example making decisions based on an ability to differentiate between pieces of information. This is smart stuff, but it's tech that the construction industry has been somewhat sluggish to seize on and apply to its own processes. But now it seems construction is waking up to the potential of AI.