Ever since Machine Learning as a Service (MLaaS) emerges as a viable business that utilizes deep learning models to generate lucrative revenue, Intellectual Property Right (IPR) has become a major concern because these deep learning models can easily be replicated, shared, and re-distributed by any unauthorized third parties. To the best of our knowledge, one of the prominent deep learning models - Generative Adversarial Networks (GANs) which has been widely used to create photorealistic image are totally unprotected despite the existence of pioneering IPR protection methodology for Convolutional Neural Networks (CNNs). This paper therefore presents a complete protection framework in both black-box and white-box settings to enforce IPR protection on GANs. Empirically, we show that the proposed method does not compromise the original GANs performance (i.e. image generation, image super-resolution, style transfer), and at the same time, it is able to withstand both removal and ambiguity attacks against embedded watermarks.

In this paper I explore the scaffolding of normative assumptions that supports Sabina Leonelli's implicit appeal to the values of epistemic integrity and the global public good that conjointly animate the ethos of responsible and sustainable data work in the context of COVID-19. Drawing primarily on the writings of sociologist Robert K. Merton, the thinkers of the Vienna Circle, and Charles Sanders Peirce, I make some of these assumptions explicit by telling a longer story about the evolution of social thinking about the normative structure of science from Merton's articulation of his well-known norms (those of universalism, communism, organized skepticism, and disinterestedness) to the present. I show that while Merton's norms and his intertwinement of these with the underlying mechanisms of democratic order provide us with an especially good starting point to explore and clarify the commitments and values of science, Leonelli's broader, more context-responsive, and more holistic vision of the epistemic integrity of data scientific understanding, and her discernment of the global and biospheric scope of its moral-practical reach, move beyond Merton's schema in ways that effectively draw upon important critiques. Stepping past Merton, I argue that a combination of situated universalism, methodological pluralism, strong objectivity, and unbounded communalism must guide the responsible and sustainable data work of the future.

The new anthology The Best American Science Fiction and Fantasy 2020 includes stories from leading authors such as Victor LaValle, Rebecca Roanhorse, and Charlie Jane Anders. Tobias S. Buckell, whose story "The Galactic Tourist Industrial Complex" appears in the book, was particularly impressed with Ken Liu's story "Thoughts and Prayers." "Ken is really a complete master of the short form," Buckell says in Episode 452 of the Geek's Guide to the Galaxy podcast. "It's always a pleasure to read a Ken story." "Thoughts and Prayers" is about a mother and father who advocate for gun control following their daughter's murder only to find themselves targeted by internet trolls who harass them with violent deepfakes of their daughter.

We propose to build directly upon our longstanding, prior r&d in AI/machine ethics in order to attempt to make real the blue-sky idea of AI that can thwart mass shootings, by bringing to bear its ethical reasoning. The r&d in question is overtly and avowedly logicist in form, and since we are hardly the only ones who have established a firm foundation in the attempt to imbue AI's with their own ethical sensibility, the pursuit of our proposal by those in different methodological camps should, we believe, be considered as well. We seek herein to make our vision at least somewhat concrete by anchoring our exposition to two simulations, one in which the AI saves the lives of innocents by locking out a malevolent human's gun, and a second in which this malevolent agent is allowed by the AI to be neutralized by law enforcement. Along the way, some objections are anticipated, and rebutted.

Machine learning systems are often trained using data collected from historical decisions. If past decisions were biased, then automated systems that learn from historical data will also be biased. We propose a black-box approach to identify and remove biased training data. Machine learning models trained on such debiased data (a subset of the original training data) have low individual discrimination, often 0%. These models also have greater accuracy and lower statistical disparity than models trained on the full historical data. We evaluated our methodology in experiments using 6 real-world datasets. Our approach outperformed seven previous approaches in terms of individual discrimination and accuracy.

The data protection authority in Hamburg, Germany, for instance, last week issued a preliminary order saying New York-based Clearview must delete biometric data related to Matthias Marx, a 32-year-old doctoral student. The regulator ordered the company to delete biometric hashes, or bits of code, used to identify photos of Mr. Marx's face, and gave it till Feb. 12 to comply. Not all photos, however, are considered sensitive biometric data under the European Union's 2018 General Data Protection Regulation. The action in Germany is only one of many investigations, lawsuits and regulatory reprimands that Clearview is facing in jurisdictions around the world. On Wednesday, Canadian privacy authorities called the company's practices a form of "mass identification and surveillance" that violated the country's privacy laws.

Two Google engineers have quit over the treatment of Timnit Gebru, a prominent Black artificial intelligence researcher whose exit from the company sparked widespread outrage in the tech industry. David Baker, an engineering director focused on user safety, left Google last month after 16 years because Gebru's departure "extinguished my desire to continue as a Googler", he said in a letter seen by Reuters. Baker added: "We cannot say we believe in diversity, and then ignore the conspicuous absence of many voices from within our walls." Vinesh Kannan, a software engineer, said on Wednesday that he had also left the company this week because Google had mistreated Gebru and April Christina Curley, a Black recruiter who has said she was wrongly fired from Google last year. "They were wronged," Kannan said in a tweet.

Artificial intelligence is hardly confined by international borders, as businesses, universities, and governments tap a global pool of ideas, algorithms, and talent. Yet the AI programs that result from this global gold rush can still reflect deep cultural divides. New research shows how government censorship affects AI algorithms--and can influence the applications built with those algorithms. Margaret Roberts, a political science professor at UC San Diego, and Eddie Yang, a PhD student there, examined AI language algorithms trained on two sources: the Chinese-language version of Wikipedia, which is blocked within China; and Baidu Baike, a similar site operated by China's dominant search engine, Baidu, that is subject to government censorship. Baidu did not respond to a request for comment.

Chapter 5 of this free 15 chapter AI handbook provides an overview of natural language processing.

The facial recognition app Clearview AI is not welcome in Canada and the company that developed it should delete Canadians' faces from its database, the country's privacy commissioner said on Wednesday. "What Clearview does is mass surveillance, and it is illegal," Commissioner Daniel Therrien said at a news conference. He forcefully denounced the company as putting all of society "continually in a police lineup." Though the Canadian government does not have legal authority to enforce photo removal, the position -- the strongest one an individual country has taken against the company -- was clear: "This is completely unacceptable." Clearview scraped more than three billion photos from social media networks and other public websites in order to build a facial recognition app that is now used by over 2,400 U.S. law enforcement agencies, according to the company.