As machine learning black boxes are increasingly being deployed in domains such as healthcare and criminal justice, there is growing emphasis on building tools and techniques for explaining these black boxes in an interpretable manner. Such explanations are being leveraged by domain experts to diagnose systematic errors and underlying biases of black boxes. In this paper, we demonstrate that post hoc explanations techniques that rely on input perturbations, such as LIME and SHAP, are not reliable. Specifically, we propose a novel scaffolding technique that effectively hides the biases of any given classifier by allowing an adversarial entity to craft an arbitrary desired explanation. Our approach can be used to scaffold any biased classifier in such a way that its predictions on the input data distribution still remain biased, but the post hoc explanations of the scaffolded classifier look innocuous. Using extensive evaluation with multiple real-world datasets (including COMPAS), we demonstrate how extremely biased (racist) classifiers crafted by our framework can easily fool popular explanation techniques such as LIME and SHAP into generating innocuous explanations which do not reflect the underlying biases.

Data scientists need the ability to explain their models to executives and stakeholders, so they can understand the value and accuracy of their findings. The ability to interpret a generated model is crucial to ensure compliance with company policies, industry standards, and government regulations. Here's an interesting write up on Model Interpretability in Azure Machine Learning Services. Model designers and evaluators can use interpretability output of a model to verify hypotheses and build trust with stakeholders. They also use the insights into the model for debugging, validating model behavior matches their objectives, and to check for bias or insignificant features.

Artificial intelligence is expected to contribute a potential US$15.7 trillion to the global economy by 2030, so it's not surprising that more companies are considering buying an AI firm to bolster their tech capabilities. The number of AI deals rose from only 10 in 2012 to 166 in 2018, according to CB Insights, and there were more than 140 AI acquisitions in just the first eight months of 2019.1 If your company is thinking about jumping on the AI bandwagon, you should know that this deal won't be like others you've made in the past. So you need to prepare. For one thing, the maturity of these companies varies markedly. Six out of 10 of the approximately 1,600 European AI startups in MMC Ventures' 2019 State of AI study are only at the earliest stages of AI implementation, while only one out of six has reached the growth stage.2

We are in the midst of an AI boom, with investment and merger and acquisition activity in the sector increasing exponentially. This new frontier raises various challenges for IP law, where numerous questions exist about whether the existing legal framework is fit for purpose in the age of the intelligent machine. "Artificial intelligence" is generally used to refer to technology that carries out tasks that normally need human intelligence. Here, we focus on machine learning, a subset of AI that enables computers to learn from data without being explicitly programmed. A machine learning system typically comprises a computational model based on an algorithm (or algorithm stack) with a dataset to train it.

Machine learning and artificial intelligence are integral components of any modern organization's IT stack but these data-harvesting tools can have a dark side if appropriate risk management and planning protocols aren't in place. There's no denying the power and possibilities created by AI and machine learning. With this astounding power to build models designed to improve the efficiency and performance of everything from marketing and supply chain to sales and human resources comes considerable responsibility. A recent McKinsey report sheds some light on how companies in every industry should be wary of assuming that these relatively new and remarkably complex tools will always deliver the desired outcome as they're integrated with other applications and processes. These tools are just like every other tool that's ever existed: they're only as good as the people designing and using them.

The HR function in enterprises is in the middle of a revolution, with AI playing an increasingly important role in facilitating processes and decision-making. Many organizations now leverage AI to improve the efficiency of their recruitment, performance management and succession planning processes. AI is particularly well-suited to this role because it can serve to automate low-level decision-making (filtering CVs by comparing against a job posting, compiling language-based feedback into numeric ratings, discovering similarities between different profiles.), However, organizations that blindly adopt AI without being aware of its limitations run the risk of having a system that delivers bad or incorrect results. The impact of this can range from simply missing out on quality candidates to more serious issues like being in violation of regulatory compliance that can lead to legal trouble.

By day, Pat Scannell is a professional technologist, who has spent a 25 year career commercializing disruptive technologies into mass market adoption, and he has done this in domains ranging from Internet, Mobile, IoT and Defense. By night, he researches and writes about the cumulative effects of technology in humans, specifically the effects on how we think, now and in the future. In this article, Pat summarizes the results of his findings and invites those interested in discussing the issues in-depth to join the dialog. "For every dollar and every minute we invest in improving AI, we would be wise to invest a dollar and a minute in exploring and developing human consciousness" – Yuval Harari As CES wraps up, it's clear that the Artificial Intelligence hype is peaking, from self-driving cars, business decision software, and even the AI powered cat litter box. Our tech and our industries are going to become disrupted by the ever-accelerating technology around us, but what if our thoughts will be, too?

A few steps remain for China's plans for a military expansion into Taiwan with the incorporation of an AI Digital Brain that can connect to the 5G network to power drones, machines, robotics, surveillance systems, and total control of the Taiwanese people. Through IP theft, forced tech transfers, espionage, social engineering, open-source sharing, collaborations, investments, and mergers, China's government has put their tentacles into every country, and every domain and sphere that incorporates trade, and human existence. China's moves against Taiwan and the West links Chip Makers (TSMC) for AI guided Weapons that interconnect Huawei, Baidu, Megvii Face, Sensetime, and numerous tech companies. These tech companies interlink with social engineering of U.S democratic politicians, the penetration of KMT by Chinese and Taiwanese implants, with the laying of the massive groundwork to control Taiwan with AI automated drones, robotics & smart cities on the 5G network. This interconnection includes soft power initiative's at a global level to social engineer reporters, media, politicians, corporate tech leaders and the Chinese citizens to imprint a mental impression that China's 70 year track record of concentration camps, one party system, torture, rape, and Orwellian surveillance, will not transfer over to worldwide Chinese subjugation of humanity. The social engineering also makes the implication, "Chinese government stands by its contracts and words", yet they are breaking their agreement by invading Hong Kong and embedding soldiers within the Hong Kong populous, including Hong Kong police.

Our partner Elisabetta Papa is to speak about "Patent protection tools in the field of Artificial Intelligence and IoT – Trends in Europe and open criticalities" during the seminar "Intellectual Property in Europe and Russia – Global markets, intangibles and technology" to be held at the Milan office of law firm De Berti Jacchia Franchini Forlani on 5 November 2019 from 3.30 pm.

In late 2018, Amazon discontinued the use of their AI-based recruitment system because they found that it was biased against women. According to sources close to the matter, the tool gave low ratings to resumes with the terms "woman" or "women's" in applications for technical roles, and went as far as downgrading applicants from two all-women's colleges. This problem is not new. In 2003, the National Bureau of Economic Growth (NBER) conducted an experiment to track the presence of racial bias in hiring. In the test, they sent out two sets of fictitious resumes with identical information about education and experience.