Banks have been in the business of deciding who is eligible for credit for centuries. But in the age of artificial intelligence (AI), machine learning (ML), and big data, digital technologies have the potential to transform credit allocation in positive as well as negative directions. Given the mix of possible societal ramifications, policymakers must consider what practices are and are not permissible and what legal and regulatory structures are necessary to protect consumers against unfair or discriminatory lending practices. In this paper, I review the history of credit and the risks of discriminatory practices. I discuss how AI alters the dynamics of credit denials and what policymakers and banking officials can do to safeguard consumer lending.

But increasingly, algorithms have begun to arbitrate fairness for us. They decide who sees housing ads, who gets hired or fired, and even who gets sent to jail. Consequently, the people who create them--software engineers--are being asked to articulate what it means to be fair in their code. This is why regulators around the world are now grappling with a question: How can you mathematically quantify fairness?

Developments in Artificial Intelligence and Machine Learning (AI/ML) are rapidly creating competitive advantages for established and emerging companies across many industries. The healthcare sector is no exception, as health services, biopharmaceuticals and medical device firms are enjoying the benefits and experiencing the disruption of AI/ML. In fact, Statista projects that the market for AI in healthcare will climb from just over one billion in 2017 to more than $28 billion U.S. dollars by 2025. Our clients rely on IMS and our extensive network of best-in-class experts to provide them the foremost experts to consult, opine and often testify regarding emerging technologies, policies, and areas of disruption in critical strategic projects and complex commercial litigation. Stu Lipoff, a sought-after expert in healthcare data, cybersecurity and data privacy has been relied upon by IMS clients for many high-profile cases, notes that the introduction of Software as a Medical Device (SaMD) and AI/ML in the space are leading to complex questions.

In an age when artificial intelligence can churn out poetry, the U.S. Patent and Trademark Office is expanding its query into how intellectual property protections need to adapt to cover creations by non-humans.

The machine learning community has witnessed a surge in releases of frameworks, libraries and software. Tech pioneers like Google, Amazon, Microsoft and others have insisted their intention behind open-sourcing their technology. However, there has been a growing trend of these tech giants claiming ownership for their innovations. According to the National Bureau of Economic Research study, in 2010, there were 145 US patent filings that mentioned machine learning, compared to 594 in 2016. Google, especially, has filed patents related to machine learning and neural networks 99 times in 2016 alone.

In the past five years, Machine Learning has come a long way. You might have noticed that Siri, Alexa, and Google Assistant are way better than they used to be, or that automatic translation on websites, while still fairly spotty, is hugely improved from where it was a few years ago. But many still don't quite grasp how far we've come, and how fast. Recently, two images made the rounds that underscore the huge advances machine learning has made -- and show why we're in for a new age of mischief and online fakery. The first was put together by Ian Goodfellow, the director of machine learning at Apple's Special Projects Group and a leader in the field.

Our goal is to understand the so-called trade-off between fairness and accuracy. In this work, using a tool from information theory called Chernoff information, we derive fundamental limits on this relationship that explain why the accuracy on a given dataset often decreases as fairness increases. Novel to this work, we examine the problem of fair classification through the lens of a mismatched hypothesis testing problem, i.e., where we are trying to find a classifier that distinguishes between two "ideal" distributions but instead we are given two mismatched distributions that are biased. Based on this perspective, we contend that measuring accuracy with respect to the given (possibly biased) dataset is a problematic measure of performance. Instead one should also consider accuracy with respect to an ideal dataset that is unbiased. We formulate an optimization to find such ideal distributions and show that the optimization is feasible. Lastly, when the Chernoff information for one group is strictly less than another in the given dataset, we derive the information-theoretic criterion under which collection of more features can actually improve the Chernoff information and achieve fairness without compromising accuracy on the available data.

The Age of Intelligent Machines is upon us, and we are at a reflection point. The proliferation of fast-moving technologies, including forms of artificial intelligence, will cause us to confront profound questions about ourselves. The era of human intellectual superiority is ending, and, as a species, we need to plan for this monumental shift. A Human Algorithm: How Artificial Intelligence Is Redefining Who We Are examines the immense impact intelligent technology will have on humanity. These machines, while challenging our personal beliefs and our socio-economic world order, also have the potential to transform our health and well-being, alleviate poverty and suffering, and reveal the mysteries of intelligence and consciousness.

In February 2019 Polish government added an amendment to a banking law that gives a customer a right to receive an explanation in case of a negative credit decision. This means that a bank needs to be able to explain why the loan wasn't granted if the decision process was automatic.

Invisibility cloak technology has been developed that bends light in order to make objects disappear. The material, which was created by Canada-based camouflage company Hyperstealth, could be used to hide large items such as army tanks or even to shield troops on the ground from enemies. Amazing video footage shows the screen in all its glory – in one clip a white sheet on the screen is visible, before a small miniature tank is revealed behind the screen. This is while another clip shows the screen in front of what looks like a tree, but it comes down, revealing a large housing complex. The company has been developing the technology for a number of years but has now applied for patents to begin the process of manufacturing it.