Over the past few years, providers such as Google, Microsoft, and Amazon have started to provide customers with access to software interfaces allowing them to easily embed machine learning tasks into their applications. Overall, organizations can now use Machine Learning as a Service (MLaaS) engines to outsource complex tasks, e.g., training classifiers, performing predictions, clustering, etc. They can also let others query models trained on their data. Naturally, this approach can also be used (and is often advocated) in other contexts, including government collaborations, citizen science projects, and business-to-business partnerships. However, if malicious users were able to recover data used to train these models, the resulting information leakage would create serious issues. Likewise, if the inner parameters of the model are considered proprietary information, then access to the model should not allow an adversary to learn such parameters. In this document, we set to review privacy challenges in this space, providing a systematic review of the relevant research literature, also exploring possible countermeasures. More specifically, we provide ample background information on relevant concepts around machine learning and privacy. Then, we discuss possible adversarial models and settings, cover a wide range of attacks that relate to private and/or sensitive information leakage, and review recent results attempting to defend against such attacks. Finally, we conclude with a list of open problems that require more work, including the need for better evaluations, more targeted defenses, and the study of the relation to policy and data protection efforts.

The United States Patent and Trademark Office officially selected a new partner to support its increasing adoption of artificial intelligence and machine learning capabilities.General Dynamics Information Technology on Monday announced it was awarded a contract worth up to $50 million through its Intelligent Automation and Innovation Support Services blanket purchase agreement. GDIT is the latest of more than a dozen companies the agency tapped under the future-facing BPA. Other businesses who've made their own recent announcements detailing partnerships via the agreement include Octo and Steampunk.In the announcement, Vice President & General Manager Christopher Hegedus for GDIT's Diplomacy, Commerce and Government Operations business area noted the company's supported the agency for nearly two decades, and through this "new work, [aims to bring its] AI, ML and robotic process automation expertise to help USPTO develop solutions that accelerate the patent and trademark process to benefit American innovators." Charged with issuing patents for inventions and registering trademarks for product and intellectual property identification, USPTO is making deliberate moves to "propel" itself into the next decade technologically, the agency's chief information officer recently told Nextgov. And it appears the BPA is one avenue helping it to do exactly that.

Illinois law permits people to sue companies that don't get consent before collecting consumers' data. Attorneys that sued Facebook in 2015 alleged that the company's photo tagging feature was powered by facial recognition data used to create and store "face templates."

Machine learning algorithms are increasingly being used to make decisions about the individuals in various areas such as university admissions, employment, health, etc. As the individuals gain information about the algorithms being used, they have an incentive to adapt their data so as to be classified desirably. For example, if a student is aware that a university heavily weighs SAT score in their admission process, she will be motivated to achieve a higher SAT score either through extensive test preparation or multiple tries. Such efforts by the students might not change their probability of being successful at the university, but are enough to fool the admissions' process. Therefore, under such "strategic manipulation" of one's data, the predictive power of the decisions are bound to decrease. One way to prevent such manipulation is by keeping the classification algorithms a secret, but this is not a practical solution to the problem, as some information is bound to leak over time and the transparency of these algorithms is a growing social concern. Thus, this motivates the study of algorithms that are optimal under "strategic manipulation". The problem of gaming in the context of classification algorithms is a well known problem and is increasingly gaining researchers' attention, for example, [HMPW16, ALB16, HIV19, MMDH19, DRS

It is generally agreed that one origin of machine bias is resulting from characteristics within the dataset on which the algorithms are trained, i.e., the data does not warrant a generalized inference. We, however, hypothesize that a different `mechanism', hitherto not articulated in the literature, may also be responsible for machine's bias, namely that biases may originate from (i) the programmers' cultural background, such as education or line of work, or (ii) the contextual programming environment, such as software requirements or developer tools. Combining an experimental and comparative design, we studied the effects of cultural metaphors and contextual metaphors, and tested whether each of these would `transfer' from the programmer to program, thus constituting a machine bias. The results show (i) that cultural metaphors influence the programmer's choices and (ii) that `induced' contextual metaphors can be used to moderate or exacerbate the effects of the cultural metaphors. This supports our hypothesis that biases in automated systems do not always originate from within the machine's training data. Instead, machines may also `replicate' and `reproduce' biases from the programmers' cultural background by the transfer of cultural metaphors into the programming process. Implications for academia and professional practice range from the micro programming-level to the macro national-regulations or educational level, and span across all societal domains where software-based systems are operating such as the popular AI-based automated decision support systems.

Our work fits within a larger context of recent advances in RL. RL has been used to train AIs to win competitive games, such as Go, Dota, and Starcraft. In those settings, the RL objective is inherently adversarial ("beat-the-other-team"). Machine learning has also been used for the design of auction rules. In this work, we instead focus on the opportunity to use AI to promote social welfare through the design of optimal tax policies in dynamic economies. Many studies have shown that high income inequality can negatively impact economic growth and economic opportunity.

It automates the process by which companies and website owners make their content accessible to users with hearing, visual, and motor impairments as well as some other functional disabilities. The total funding company raised is $12.5M in funding over 2 rounds. It's an AI-based solution scans website and automatically offers key modifications to transmit data and accessible content to end-users in a manner compliant with the international and US disability standards, which includes the Web Content Accessibility Guidelines and the Americans with Disabilities Act. Executive Opinion Shir Ekerling, Co-founder and CEO of accessiBe, said, "What excites us most about our partnership with K1 is that now, with the amazing support of our investors, we can bring accessibility to the world. Our vision is to make the internet truly accessible to everyone. By utilizing machine learning, our solution can help millions of businesses comply with legislation and avoid lawsuits on the one hand, while enabling users with disabilities to browse the internet effectively on the other. Mike Velcich, Principal at K1, said, "K1 is excited to partner with Shir Ekerling and the accessiBe team as they continue their rapid global expansion.

Artificial intelligence has become an integral part of everything from medical diagnostics technology to systems that analyze electoral candidates and provide accurate information to voters. However, there are still many AI skeptics, and especially those who question the role of AI in the justice system. Many legal leaders and institutions are curious about the efficiency benefits AI brings to the field. But the big question is: can AI help make the judicial system fairer? Many claim that the United States' judicial system is one of the most robust in the world.

Is Artificial intelligence (A.I) a revolution or a war? Do we really need more metaphors to describe it? Nowadays, A.I dictates what information is presented to us on social media, which ads we see, and what prices we're offered both on and offline. An algorithm can technically write and analyse books, beat humans at about every game conceivable, make movies, compose classical songs and help magicians perform better tricks. Beyond the arts, it also has the potential to encourage better decision-making, make medical diagnoses, and even solve some of humanity's most pressing challenges. It's intertwining with criminal justice, retail, education, recruiting, healthcare, banking, farming, transportation, warfare, insurance, media… the list goes on. Yet, we're so often busy discussing the ins and outs of whether A.I CAN do something, that we seldom ask if we SHOULD design it at all.

This book discusses the necessity and perhaps urgency for the regulation of algorithms on which new technologies rely; technologies that have the potential to re-shape human societies. From commerce and farming to medical care and education, it is difficult to find any aspect of our lives that will not be affected by these emerging technologies. At the same time, artificial intelligence, deep learning, machine learning, cognitive computing, blockchain, virtual reality and augmented reality, belong to the fields most likely to affect law and, in particular, administrative law. The book examines universally applicable patterns in administrative decisions and judicial rulings. First, similarities and divergence in behavior among the different cases are identified by analyzing parameters ranging from geographical location and administrative decisions to judicial reasoning and legal basis. As it turns out, in several of the cases presented, sources of general law, such as competition or labor law, are invoked as a legal basis, due to the lack of current specialized legislation. This book also investigates the role and significance of national and indeed supranational regulatory bodies for advanced algorithms and considers ENISA, an EU agency that focuses on network and information security, as an interesting candidate for a European regulator of advanced algorithms. Lastly, it discusses the involvement of representative institutions in algorithmic regulation.