Automated decision making systems are increasingly being used in real-world applications. In these systems for the most part, the decision rules are derived by minimizing the training error on the available historical data. Therefore, if there is a bias related to a sensitive attribute such as gender, race, religion, etc. in the data, say, due to cultural/historical discriminatory practices against a certain demographic, the system could continue discrimination in decisions by including the said bias in its decision rule. We present an information theoretic framework for designing fair predictors from data, which aim to prevent discrimination against a specified sensitive attribute in a supervised learning setting. We use equalized odds as the criterion for discrimination, which demands that the prediction should be independent of the protected attribute conditioned on the actual label. To ensure fairness and generalization simultaneously, we compress the data to an auxiliary variable, which is used for the prediction task. This auxiliary variable is chosen such that it is decontaminated from the discriminatory attribute in the sense of equalized odds. The final predictor is obtained by applying a Bayesian decision rule to the auxiliary variable.

Machine learning vs. deep learning isn't exactly a boxing knockout – deep learning is a subset of machine learning, and both are subsets of artificial intelligence (AI). However, there is a lot of confusion in the marketplace around the definitions and use cases of machine learning and deep learning, so let's clear up the confusion. Computers identify and act upon data patterns, and over time learn to improve their accuracy without explicit programming. Machine learning is behind analytics like predictive coding, clustering, and visual heat maps. Deep learning computer networks simulate the way a human brain perceives, organizes, and makes decisions from data input.

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Here's one of the crucial issues of criminal justice -- although you haven't been found guilty of a crime, you have found yourself mixed up in the criminal justice system, and the impacts on your life are already manifesting themselves. The average elapsed time between arrest and conviction is six months. It's fairly likely that you lose your job. It's also possible you could lose custody of your children, get behind on bills causing damage to your credit rating; you could even lose your home. This impact is especially devastating for those people who are already struggling to make ends meet.

The Situation: Artificial intelligence ("AI") technology is exploding across virtually all industries. Technology companies are innovating at warp speed, and even companies that do not principally identify as "technology companies" are becoming increasingly "high tech" in how they deliver goods and services. Innovation is outpacing the law--many aspects of AI legal protection are still open questions. The Result: With innovation comes imitation … and infringement and misappropriation. Companies must be vigilant in protecting and enforcing their intellectual property ("IP") rights in AI.

Let's make one thing clear: one year isn't going to fix decades of gender discrimination in computer science and all the problems associated with it. Recent diversity reports show that women still make up only 20 percent of engineers at Google and Facebook, and an even lower proportion at Uber. But after the parade of awful news about the treatment of female engineers in 2017--sexual harassment in Silicon Valley and a Google engineer sending out a memo to his coworkers arguing that women are biologically less adept at programming, just to name a couple--there is actually reason to believe that things are looking up for 2018, especially when it comes to AI. At first glance, AI would seem among least likely areas of programming to be friendly to women. Writing in Fast Company recently, Hanna Wallach, an AI researcher and cofounder of the Women in Machine Learning Conference, said that only 13.5 percent of those working in machine learning are female. In the midst of the #MeToo movement, researchers in artificial intelligence also dealt with sexual harassment allegations, as well as complaints that inappropriate jokes were made at a parties around NIPS, a major industry conference.

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Current machine learning systems operate, almost exclusively, in a statistical, or model-free mode, which entails severe theoretical limits on their power and performance. Such systems cannot reason about interventions and retrospection and, therefore, cannot serve as the basis for strong AI. To achieve human level intelligence, learning machines need the guidance of a model of reality, similar to the ones used in causal inference tasks. To demonstrate the essential role of such models, I will present a summary of seven tasks which are beyond reach of current machine learning systems and which have been accomplished using the tools of causal modeling.

NEW YORK, NY--(Marketwired - January 09, 2018) - Artificial intelligence is the big buzzword now. Academic studies trumpet that A.I. will replace humans in the workplace at unprecedented rates, while seemingly every start-up and legacy brand touts A.I. to describe its business, hoping to make what it does sound compelling and unique. Artificial intelligence, however, is not quite as intelligent as it has been claimed to be. A.I. will dramatically improve the lives of humans, if implemented ethically, but reports in the Wall Street Journal, the New York Times, and other reputable media have documented that A.I. can be biased, sexist, racist, inexplicable, wrong, and, just plain stupid. Neural networks are as "intelligent" as the historical data and human-imposed-rules upon which they train.

TD Bank Group has announced the acquisition of Layer 6 Inc. ("Layer 6"), a world-renowned artificial intelligence (AI) company based in Toronto, Ontario. Layer 6 has emerged as a global thought-leader and pioneer in the delivery of responsive, personalized and insight-driven experiences for the financial services industry. Layer 6 founders Tomi Poutanen and Jordan Jacobs are also co-founders of the Vector Institute, a world leader in AI research and education that TD also supports. "Anticipating and meeting customer needs are at the heart of our promise, and we are excited to further accelerate our innovation agenda to deliver well into the future. As we deploy new solutions, we will extend our deep relationship with customers across all of our platforms and offer personalized, connected and legendary experiences for our customers in the digital age."