This paper studies the decentralized optimization and learning problem where multiple interconnected agents aim to learn an optimal decision function defined over a reproducing kernel Hilbert (RKH) space by jointly minimizing a global objective function, with access to locally observed data only. As a non-parametric approach, kernel learning faces a major challenge in distributed implementation: the decision variables of local objective functions are data-dependent with different sizes and thus cannot be optimized under the decentralized consensus framework without any raw data exchange among agents. To circumvent this major challenge and preserve data privacy, we leverage the random feature (RF) approximation approach to map the large-volume data represented in the RKH space into a smaller RF space, which facilitates the same-size parameter exchange and enables distributed agents to reach consensus on the function decided by the parameters in the RF space. For fast convergent implementation, we design an iterative algorithm for Decentralized Kernel Learning via Alternating direction method of multipliers (DKLA). Further, we develop a COmmunication-censored KErnel learning (COKE) algorithm to reduce the communication load in DKLA. To do so, we apply a communication-censoring strategy, which prevents an agent from transmitting at every iteration unless its local updates are deemed informative. Theoretical results in terms of linear convergence guarantee and generalization performance analysis of DKLA and COKE are provided. Comprehensive tests with both synthetic and real datasets are conducted to verify the communication efficiency and learning effectiveness of COKE.

The goal of regression analysis is to predict the value of a numeric outcome variable y given a vector of joint values of other (predictor) variables x. Usually a particular x-vector does not specify a repeatable value for y, but rather a probability distribution of possible y--values, p(y|x). This distribution has a location, scale and shape, all of which can depend on x, and are needed to infer likely values for y given x. Regression methods usually assume that training data y-values are perfect numeric realizations from some well behaived p(y|x). Often actual training data y-values are discrete, truncated and/or arbitrary censored. Regression procedures based on an optimal transformation strategy are presented for estimating location, scale and shape of p(y|x) as general functions of x, in the possible presence of such imperfect training data. In addition, validation diagnostics are presented to ascertain the quality of the solutions.

The need for transparency of predictive systems based on Machine Learning algorithms arises as a consequence of their ever-increasing proliferation in the industry. Whenever black-box algorithmic predictions influence human affairs, the inner workings of these algorithms should be scrutinised and their decisions explained to the relevant stakeholders, including the system engineers, the system's operators and the individuals whose case is being decided. While a variety of interpretability and explainability methods is available, none of them is a panacea that can satisfy all diverse expectations and competing objectives that might be required by the parties involved. We address this challenge in this paper by discussing the promises of Interactive Machine Learning for improved transparency of black-box systems using the example of contrastive explanations -- a state-of-the-art approach to Interpretable Machine Learning. Specifically, we show how to personalise counterfactual explanations by interactively adjusting their conditional statements and extract additional explanations by asking follow-up "What if?" questions. Our experience in building, deploying and presenting this type of system allowed us to list desired properties as well as potential limitations, which can be used to guide the development of interactive explainers. While customising the medium of interaction, i.e., the user interface comprising of various communication channels, may give an impression of personalisation, we argue that adjusting the explanation itself and its content is more important. To this end, properties such as breadth, scope, context, purpose and target of the explanation have to be considered, in addition to explicitly informing the explainee about its limitations and caveats...

The "Hey, Update My Voice" movement, in partnership with UNESCO, was born out of this context with the goal of teaching respect towards virtual assistants and, in addition, asking tech companies to update their assistants' responses. Because if that happens to them, imagine what happens in real life to real women. Every day around the world, virtual assistants suffer abuse and harassment of all kinds. In Brazil, for example, Lu, the virtual assistant of Magazine Luiza stores, has been victimized by this sort of violence. Worldwide, cases have been reported involving Siri and Alexa, among others.

Over the past few years, the Canada Revenue Agency has been using data analytics and AI, such as machine-learning algorithms that predict tax non-compliance and detect activity in the underground economy. Since 2018, the Department of Justice Canada has licensed the use of Tax Foresight, AI software developed by Blue J Legal Inc. in Toronto, which employs machine learning to predict – with about 90% accuracy, according to the company – how a court might rule on a particular tax scenario. "It's not just about speeding up [analysis] that would otherwise happen," says Benjamin Alarie, co-founder and CEO of Blue J Legal and Osler Chair of Business Law at the University of Toronto. "It's about making [widely] available a really good prediction that would otherwise be the domain of an experienced [lawyer]." AI technology could bring more certainty to the interpretation of tax law, Alarie adds: "Everyone benefits from that."

The pace of progress in artificial intelligence is scaring many people, that feel threatened by the huge impact automation might have on employment, and other areas such as the development of autonomous weapons. A question growing in the heads of experts, deeply aware of the impact of AI on society, is how to understand and predict what can happen, when increasingly automated complex systems fail, or go off track. As John Danaher wrote in the Institute for Ethics & Emerging Technologies, "Artificial intelligence is a classic risk/reward technology. If developed safely and properly, it could be a great boon. Trying to deliver some answers to this and other questions, Carnegie Mellon University just launched a new center, entitled K&L Gates Endowment for Ethics and Computational Technologies.

Garry Kasparov has warned that any attempts by the Government to regulate artificial intelligence (AI) could "stifle" its development and give Russia and China an advantage. The former world chess champion has become an advocate for AI development following his resignation from professional chess in 2005. He told The Telegraph that "the government should be involved" in helping researchers and private firms to develop AI in order to "pave the road" for the technology. However, he cautioned against governments attempting to regulate the technology too closely. "It's too early for the government to interfere," he said.

The term "Artificial Intelligence" conjures, in many, an image of an anthropomorphized Terminator-esque killer robot apocalypse. Hollywood movies, in recent decades, have served to only further this notion. Physicists and moral philosophers like Max Tegmark and Sam Harris, however, claim we need not fear a runaway superintelligence to adequately worry about the deleterious effects endemic to the AI space, but rather that competence on behalf of machines is a sufficiently frightening springboard from which an irreversibly harmful future can be launched. That said, there are currently a number of far more nefarious, insidious, and relevant ethical dilemmas which warrant our attention. In a world increasingly controlled by automated processes, rapidly approaching is a time in which adaptive, self-improving algorithms guide or even dictate most of the decisions that define human experience.

Does your company have an AI ethics officer? In 2014, Stephen Hawking said that AI would be humankind's best or last invention. Six years later, as we welcome 2020, companies are looking at how to use Artificial Intelligence (AI) in their business to stay competitive. The question they are facing is how to evaluate whether the AI products they use will do more harm than good. Many public and private leaders worldwide are thinking about how to address these questions around the safety, privacy, accountability transparency and bias in algorithms.

Under the new law, companies must explain how the technology works and how the tools evaluate a candidate. Employers must obtain consent from applicants before using A.I. to assess their videos. The legislation also prohibits businesses from sharing submitted videos except with "persons whose expertise or technology" are required to screen applicants. Job applicants can ask to have submitted videos destroyed, and companies, including any individual with copies, must comply within 30 days.