Stefaan Verhulst
Book by Virginia Dignum: “In this book, the author examines the ethical implications of Artificial Intelligence systems as they integrate and replace traditional social structures in new sociocognitive-technological environments. She discusses issues related to the integrity of researchers, technologists, and manufacturers as they design, construct, use, and manage artificially intelligent systems; formalisms for reasoning about moral decisions as part of the behavior of artificial autonomous systems such as agents and robots; and design methodologies for social agents based on societal, moral, and legal values.
Throughout the book the author discusses related work, conscious of both classical, philosophical treatments of ethical issues and the implications in modern, algorithmic systems, and she combines regular references and footnotes with suggestions for further reading. This short overview is suitable for undergraduate students, in both technical and non-technical courses, and for interested and concerned researchers, practitioners, and citizens….(More)”.
Book by Rikke Frank Jørgensen: “Today such companies as Apple, Facebook, Google, Microsoft, and Twitter play an increasingly important role in how users form and express opinions, encounter information, debate, disagree, mobilize, and maintain their privacy. What are the human rights implications of an online domain managed by privately owned platforms? According to the Guiding Principles on Business and Human Rights, adopted by the UN Human Right Council in 2011, businesses have a responsibility to respect human rights and to carry out human rights due diligence. But this goal is dependent on the willingness of states to encode such norms into business regulations and of companies to comply. In this volume, contributors from across law and internet and media studies examine the state of human rights in today’s platform society.
The contributors consider the “datafication” of society, including the economic model of data extraction and the conceptualization of privacy. They examine online advertising, content moderation, corporate storytelling around human rights, and other platform practices. Finally, they discuss the relationship between human rights law and private actors, addressing such issues as private companies’ human rights responsibilities and content regulation…(More)”.
Editorial Team of the Financial Times: “In his dystopian novel 1984, George Orwell warned of a future under the ever vigilant gaze of Big Brother. Developments in surveillance technology, in particular facial recognition, mean the prospect is no longer the stuff of science fiction.
In China, the government was this year found to have used facial recognition to track the Uighurs, a largely Muslim minority. In Hong Kong, protesters took down smart lamp posts for fear of their actions being monitored by the authorities. In London, the consortium behind the King’s Cross development was forced to halt the use of two cameras with facial recognition capabilities after regulators intervened. All over the world, companies are pouring money into the technology.
At the same time, governments and law enforcement agencies of all hues are proving willing buyers of a technology that is still evolving — and doing so despite concerns over the erosion of people’s privacy and human rights in the digital age. Flaws in the technology have, in certain cases, led to inaccuracies, in particular when identifying women and minorities.
The news this week that Chinese companies are shaping new standards at the UN is the latest sign that it is time for a wider policy debate. Documents seen by this newspaper revealed Chinese companies have proposed new international standards at the International Telecommunication Union, or ITU, a Geneva-based organisation of industry and official representatives, for things such as facial recognition. Setting standards for what is a revolutionary technology — one recently described as the “plutonium of artificial intelligence” — before a wider debate about its merits and what limits should be imposed on its use, can only lead to unintended consequences. Crucially, standards ratified in the ITU are commonly adopted as policy by developing nations in Africa and elsewhere — regions where China has long wanted to expand its influence. A case in point is Zimbabwe, where the government has partnered with Chinese facial recognition company CloudWalk Technology. The investment, part of Beijing’s Belt and Road investment in the country, will see CloudWalk technology monitor major transport hubs. It will give the Chinese company access to valuable data on African faces, helping to improve the accuracy of its algorithms….
Progress is needed on regulation. Proposals by the European Commission for laws to give EU citizens explicit rights over the use of their facial recognition data as part of a wider overhaul of regulation governing artificial intelligence are welcome. The move would bolster citizens’ protection above existing restrictions laid out under its general data protection regulation. Above all, policymakers should be mindful that if the technology’s unrestrained rollout continues, it could hold implications for other, potentially more insidious, innovations. Western governments should step up to the mark — or risk having control of the technology’s future direction taken from them….(More)”.
Essay by Jamie Grace: “This essay is an introductory exploration of machine learning technologies and their inherent human rights issues in criminal justice contexts. These inherent human rights issues include privacy concerns, the chilling of freedom of expression, problems around potential for racial discrimination, and the rights of victims of crime to be treated with dignity.
This essay is built around three case studies – with the first on the digital ‘mining’ of rape complainants’ mobile phones for evidence for disclosure to defence counsel. This first case study seeks to show how AI or machine learning tech might hypothetically either ease or inflame some of the tensions involved for human rights in this context. The second case study is concerned with the human rights challenges of facial recognition of suspects by police forces, using automated algorithms (live facial recognition) in public places. The third case study is concerned with the development of useful self-regulation in algorithmic governance practices in UK policing. This essay concludes with an emphasis on the need for the ‘politics of information’ (Lyon, 2007) to catch up with the ‘politics of public protection’ (Nash, 2010)….(More)”.
Book edited by Karen Yeung and Martin Lodge: “As the power and sophistication of of ‘big data’ and predictive analytics has continued to expand, so too has policy and public concern about the use of algorithms in contemporary life. This is hardly surprising given our increasing reliance on algorithms in daily life, touching policy sectors from healthcare, transport, finance, consumer retail, manufacturing education, and employment through to public service provision and the operation of the criminal justice system. This has prompted concerns about the need and importance of holding algorithmic power to account, yet it is far from clear that existing legal and other oversight mechanisms are up to the task. This collection of essays, edited by two leading regulatory governance scholars, offers a critical exploration of ‘algorithmic regulation’, understood both as a means for co-ordinating and regulating social action and decision-making, as well as the need for institutional mechanisms through which the power of algorithms and algorithmic systems might themselves be regulated. It offers a unique perspective that is likely to become a significant reference point for the ever-growing debates about the power of algorithms in daily life in the worlds of research, policy and practice. The range of contributors are drawn from a broad range of disciplinary perspectives including law, public administration, applied philosophy, data science and artificial intelligence.
Taken together, they highlight the rise of algorithmic power, the potential benefits and risks associated with this power, the way in which Sheila Jasanoff’s long-standing claim that ‘technology is politics’ has been thrown into sharp relief by the speed and scale at which algorithmic systems are proliferating, and the urgent need for wider public debate and engagement of their underlying values and value trade-offs, the way in which they affect individual and collective decision-making and action, and effective and legitimate mechanisms by and through which algorithmic power is held to account….(More)”.
Collection of Essays by NL Digital Government: “Smart cities are urban areas where large amounts of data are collected using sensors to enable a range of processes in the cities to run smoothly. However, the use of data is only legally and ethically allowed if the data is gathered and processed in a proper manner. It is not clear to many cities what data (personal or otherwise) about citizens may be gathered and processed, and under what conditions. The main question addressed by this essay concerns the degree to which data on citizens may be reused in the context of smart cities.
The emphasis here is on the reuse of data. Among the aspects featured are smart cities, the Internet of Things, big data, and nudging. Diferent types of data reuse will also be identifed using a typology that helps clarify and assess the desirability of data reuse. The heart of this essay is an examination of the most relevant legal and ethical frameworks for data reuse.
The most relevant legal frameworks are privacy and human rights, the protection of personal data and administrative law (in particular, the general principles of sound administration). The most relevant ethical frameworks are deontology, utilitarianism, and value ethics. The ethical perspectives ofer assessment frameworks that can be used within the legal frameworks, for drawing up codes of conduct, for example, and other forms of self-regulation. Observance of the legal and ethical frameworks referred to in this essay very probably means that data is being used and reused in an appropriate manner. Failure to observe these frameworks means that such use and reuse is not appropriate.
Four recommendations are made on the basis of these conclusions. Local authorities in smart cities must commit themselves to the appropriate reuse of data through public-private partnerships, actively involve citizens in their considerations of what factors are relevant, ensure transparency on data-related matters and in such considerations, and gradually continue the development of smart cities through pilot schemes….(More)”.
CRS Report: “Artificial intelligence (AI) is a rapidly growing field of technology with potentially significant implications for national security. As such, the U.S. Department of Defense (DOD) and other nations are developing AI applications for a range of military functions. AI research is underway in the fields of intelligence collection and analysis, logistics, cyber operations, information operations, command and control, and in a variety of semiautonomous and autonomous vehicles.
Already, AI has been incorporated into military operations in Iraq and Syria. Congressional action has the potential to shape the technology’s development further, with budgetary and legislative decisions influencing the growth of military applications as well as the pace of their adoption.
AI technologies present unique challenges for military integration, particularly because the bulk of AI development is happening in the commercial sector. Although AI is not unique in this regard, the defense acquisition process may need to be adapted for acquiring emerging technologies like AI. In addition, many commercial AI applications must undergo significant modification prior to being functional for the military.
A number of cultural issues also challenge AI acquisition, as some commercial AI companies are averse to partnering with DOD due to ethical concerns, and even within the department, there can be resistance to incorporating AI technology into existing weapons systems and processes.
Potential international rivals in the AI market are creating pressure for the United States to compete for innovative military AI applications. China is a leading competitor in this regard, releasing a plan in 2017 to capture the global lead in AI development by 2030. Currently, China is primarily focused on using AI to make faster and more well-informed decisions, as well as on developing a variety of autonomous military vehicles. Russia is also active in military AI development, with a primary focus on robotics.
Although AI has the potential to impart a number of advantages in the military context, it may also introduce distinct challenges. AI technology could, for example, facilitate autonomous operations, lead to more informed military decisionmaking, and increase the speed and scale of military action. However, it may also be unpredictable or vulnerable to unique forms of manipulation. As a result of these factors, analysts hold a broad range of opinions on how influential AI will be in future combat operations. While a small number of analysts believe that the technology will have minimal impact, most believe that AI will have at least an evolutionary—if not revolutionary—effect….(More)”.
Report by Unesco: “Artificial Intelligence (AI) is increasingly becoming the veiled decision-maker of our times. The diverse technical applications loosely associated with this label drive more and more of our lives. They scan billions of web pages, digital trails and sensor-derived data within micro-seconds, using algorithms to prepare and produce significant decisions.
AI and its constitutive elements of data, algorithms, hardware, connectivity and storage exponentially increase the power of Information and Communications Technology (ICT). This is a major opportunity for Sustainable Development, although risks also need to be addressed.
It should be noted that the development of AI technology is part of the wider ecosystem of Internet and other advanced ICTs including big data, Internet of Things, blockchains, etc. To assess AI and other advanced ICTs’ benefits and challenges – particularly for communications and information – a useful approach is UNESCO’s Internet Universality ROAM principles.These principles urge that digital development be aligned with human Rights, Openness, Accessibility and Multi-stakeholder governance to guide the ensemble of values, norms, policies, regulations, codes and ethics that govern the development and use of AI….(More)”
A special section of Internet Policy Review edited by Christian Katzenbach and Thomas Christian Bächle: “With this new special section Defining concepts of the digital society in Internet Policy Review, we seek to foster a platform that provides and validates exactly these overarching frameworks and theories. Based on the latest research, yet broad in scope, the contributions offer effective tools to analyse the digital society. Their authors offer concise articles that portray and critically discuss individual concepts with an interdisciplinary mindset. Each article contextualises their origin and academic traditions, analyses their contemporary usage in different research approaches and discusses their social, political, cultural, ethical or economic relevance and impact as well as their analytical value. With this, the authors are building bridges between the disciplines, between research and practice as well as between innovative explanations and their conceptual heritage….(More)”
Algorithmic governance
Christian Katzenbach, Alexander von Humboldt Institute for Internet and Society
Lena Ulbricht, Berlin Social Science Center
Datafication
Ulises A. Mejias, State University of New York at Oswego
Nick Couldry, London School of Economics & Political Science
Filter bubble
Axel Bruns, Queensland University of Technology
Platformisation
Thomas Poell, University of Amsterdam
David Nieborg, University of Toronto
José van Dijck, Utrecht University
Privacy
Tobias Matzner, University of Paderborn
Carsten Ochs, University of Kassel
Book by Miguel A. Hernán, James M. Robins: “Causal Inference is an admittedly pretentious title for a book. Causal inference is a complex scientific task that relies on triangulating evidence from multiple sources and on the application of a variety of methodological approaches. No book can possibly provide a comprehensive description of methodologies for causal inference across the sciences. The authors of any Causal Inference book will have to choose which aspects of causal inference methodology they want to emphasize.
The title of this introduction reflects our own choices: a book that helps scientists–especially health and social scientists–generate and analyze data to make causal inferences that are explicit about both the causal question and the assumptions underlying the data analysis. Unfortunately, the scientific literature is plagued by studies in which the causal question is not explicitly stated and the investigators’ unverifiable assumptions are not declared. This casual attitude towards causal inference has led to a great deal of confusion. For example, it is not uncommon to find studies in which the effect estimates are hard to interpret because the data analysis methods cannot appropriately answer the causal question (were it explicitly stated) under the investigators’ assumptions (were they declared).
In this book, we stress the need to take the causal question seriously enough to articulate it, and to delineate the separate roles of data and assumptions for causal inference. Once these foundations are in place, causal inferences become necessarily less casual, which helps prevent confusion. The book describes various data analysis approaches that can be used to estimate the causal effect of interest under a particular set of assumptions when data are collected on each individual in a population. A key message of the book is that causal inference cannot be reduced to a collection of recipes for data analysis.
The book is divided in three parts of increasing difficulty: Part I is about causal inference without models (i.e., nonparametric identification of causal effects), Part II is about causal inference with models (i.e., estimation of causal effects with parametric models), and Part III is about causal inference from complex longitudinal data (i.e., estimation of causal effects of time-varying treatments)….(More) (Additional Material)”.