Turning ethical Artificial Intelligence into reality implies assessing the risks of AI in context, particularly in terms of its impact on civil and social rights and then, depending on the assessed risk, defining standards or regulating the ethical design, development and implementation of algorithmic systems. This is the aim of this introductory handbook by the Council of Europe and the Alan Turing Institute, of late 2021, "Artificial Intelligence, Human Rights, Democracy and the Rule of Law: A Primer". A key initiative in this process was the feasibility study prepared and approved in December by the Council of Europe's Ad Hoc Committee on Artificial Intelligence (CAHAI), which explores options for an international legal response, based on Council of Europe standards in the fields of artificial intelligence, rights, democracy and the rule of law: it proposes nine principles and priorities that are well suited to the new challenges posed by the design, development and deployment of Artificial Intelligence systems. When codified into law, these principles and priorities create a set of interconnected rights and obligations that will work to ensure that the design and use of artificial intelligence technologies conform to the values of human rights, democracy and the rule of law. The key question is whether there are responses to the specific risks and opportunities presented by AI systems that can and should be addressed through the use of binding and non-binding international legal instruments, through the agency of the Council of Europe, which is the guardian of the European Convention on Human Rights, Convention 108, which protects the processing of personal data, and the European Social Charter.

Machine learning and data science are key tools in science, public policy, and the design of products and services thanks to the increasing affordability of collecting, storing, and processing large quantities of data. But centralized collection can expose individuals to privacy risks and organizations to legal risks if data is not properly managed. Starting with early work in 2016,13,15 an expanding community of researchers has explored how data ownership and provenance can be made first-class concepts in systems for learning and analytics in areas now known as federated learning (FL) and federated analytics (FA). With this expanding community, interest has broadened from the initial work on federations of mobile devices to include FL across organizational silos, Internet of Things (IoT) devices, and more. In light of this, Kairouz et al.10 proposed a broader definition: Federated learning is a machine learning setting where multiple entities (clients) collaborate in solving a machine learning problem, under the coordination of a central server or service provider. Each client's raw data is stored locally and not exchanged or transferred; instead, focused updates intended for immediate aggregation are used to achieve the learning objective. An approach very similar in both philosophy and implementation, federated analytics17 can be taken to allow data scientists to generate analytical insight from the combined information in decentralized datasets. While the focus here is on FL, much of the discussion on technology and privacy applies equally well to FA use cases.

Artificial intelligence (AI) systems employ learning algorithms that adapt to their users and environment, with learning either pre-trained or allowed to adapt during deployment. Because AI can optimize its behavior, a unit's factory model behavior can diverge after release, often at the perceived expense of safety, reliability, and human controllability. Since the Industrial Revolution, trust has ultimately resided in regulatory systems set up by governments and standards bodies. Research into human interactions with autonomous machines demonstrates a shift in the locus of trust: we must trust non-deterministic systems such as AI to self-regulate, albeit within boundaries. This radical shift is one of the biggest issues facing the deployment of AI in the European region.

The European Commission (EC) has acknowledged the importance artificial intelligence (AI) plays in forming Europe's future, identifying AI as the most strategic technology of the 21st century.a With a recent proposal on a Regulation Laying Down Harmonised Rules on Artificial Intelligenceb (EU Regulatory Framework for AI), the EC aims at introducing the first comprehensive legal framework on AI, which will identify specific risks for AI, provide a collection of high-risk application domains, propose specific requirements that AI systems should meet when used in such domains, and define obligations for users and providers (U.S. regulatory development relating to AIc). What clearly emerges from these efforts is the need for an AI that behaves in a responsible way. A clear and globally accepted definition of responsibility for AI systems is still under development, but will likely include notions such as fairness, security and privacy, explain-ability, safety, and reproducibility. Although safety and reproducibility are fundamental issues in AI research and its industrial application, we will not cover them here since they are requirements in many areas of technology, therefore not specific to AI.

Advances in AI, especially based on machine learning, have provided a powerful way to extract useful patterns from large, heterogeneous data sources. The rise in massive amounts of data, coupled with powerful computing capabilities, makes it possible to tackle previously intractable real-world problems. Medicine, business, government, and science are rapidly automating decisions and processes using machine learning. Unlike traditional AI approaches based on explicit rules expressing domain knowledge, machine learning often lacks explicit human-understandable specification of the rules producing model outputs. With growing reliance on automated decisions, an overriding concern is understanding the process by which "black box" AI techniques make decisions.

Welcome to the second Communications Regional Special Section spotlighting European countries and Israel. On a relatively small portion of the Earth, this region includes almost 50 countries with enormous cultural and socioeconomic diversity that is also reflected in the richness of its business structures and computer science research. The first Hot Topic article in this section illustrates the high overall share of European public research on a global scale, and further highlights significant differences within the region. We are happy to report the authors in this special section represent 15 countries throughout Europe plus Israel. An important goal emphasized by the European Union (E.U.) and many individual countries is to attain digital sovereignty of the private and public sectors, while further developing areas of traditional industrial and design strengths into the future.

Machine learning has emerged as a valuable tool for spotting patterns and trends that might otherwise escape humans. The technology, which can build elaborate models based on everything from personal preferences to facial recognition, is used widely to understand behavior, spot patterns and trends, and make informed predictions. Yet for all the gains, there is also plenty of pain. A major problem associated with machine learning is that once an algorithm or model exists, expunging individual records or chunks of data is extraordinarily difficult. In most cases, it is necessary to retrain the entire model--sometimes with no assurance that that model will not continue to incorporate the suspect data in some way, says Gautam Kamath, an assistant professor in the David R. Cheriton School of Computer Science at the University of Waterloo in Canada.

But when the technology is used to identify suspects in criminal cases, those flaws in the system can have catastrophic, life-changing consequences. People can get wrongly identified, arrested, and convicted, often without ever being told they were ID'd by a computer. It's especially troubling when you consider false identifications disproportionately affect women, young people, and people with dark skin--basically everyone other than white men. This content can also be viewed on the site it originates from. This week on Gadget Lab, WIRED senior writer Khari Johnson joins us to talk about the limits of facial recognition tech, and what happens to the people who get misidentified.

The Federal Trade Commission has struggled over the years to find ways to combat deceptive digital data practices using its limited set of enforcement options. Now, it's landed on one that could have a big impact on tech companies: algorithmic destruction. And as the agency gets more aggressive on tech by slowly introducing this new type of penalty, applying it in a settlement for the third time in three years could be the charm. In a March 4 settlement order, the agency demanded that WW International -- formerly known as Weight Watchers -- destroy the algorithms or AI models it built using personal information collected through its Kurbo healthy eating app from kids as young as 8 without parental permission. The agency also fined the company $1.5 million and ordered it to delete the illegally harvested data.

You're reading the web edition of STAT Health Tech, our guide to how tech is transforming the life sciences. Sign up to get this newsletter delivered in your inbox every Tuesday and Thursday. A growing patchwork of state-level privacy laws could pose a challenge for upstart health tech companies. After California led the way with its consumer privacy law in 2018, Virginia and Colorado followed suit, and now Massachusetts has advanced its own data privacy bill. Each independent piece of legislation could impact consumer-oriented health apps that don't fall under HIPAA -- leading digital health companies to worry about mounting costs to navigate the regulatory thicket and declining revenue for resale of consumer data, Mohana reports.