Zuboff vividly brings to life the consequences as surveillance capitalism advances from Silicon Valley into every economic sector. Vast wealth and power are accumulated in ominous new "behavioral futures markets," where predictions about our behavior are bought and sold, and the production of goods and services is subordinated to a new "means of behavioral modification." The threat has shifted from a totalitarian Big Brother state to a ubiquitous digital architecture: a "Big Other" operating in the interests of surveillance capital. Here is the crucible of an unprecedented form of power marked by extreme concentrations of knowledge and free from democratic oversight. Zuboff's comprehensive and moving analysis lays bare the threats to twenty-first century society: a controlled "hive" of total connection that seduces with promises of total certainty for maximum profit -- at the expense of democracy, freedom, and our human future.

Artificial intelligence is being used, on a massive scale, to decide who gets hired, fired and promoted. Through whistleblower exclusives, leaked internal documents and astonishing real-world practices, journalist Hilke Schellmann reveals the secret rise of AI in the world of work. Testing them herself, she discovers that many algorithms making these high-stakes calculations do more harm than good, and traces their origins to troubling pseudoscientific ideas about people's 'true' essence. Interviewing experts, developers and ordinary workers, The Algorithm offers fascinating and alarming truths. From software analysing interviewees' facial expressions and tone of voice, to video games assessing their performance, to 'personality profiles' built from candidates' social media, almost all major employers use AI in recruitment.

Algorithms are now widely employed to make decisions that have increasingly far-reaching impacts on individuals and society as a whole ("algorithmic governance"), which could potentially lead to manipulation, biases, censorship, social discrimination, violations of privacy, property rights, and more. This has sparked a global debate on how to regulate AI and robotics ("governance of algorithms"). This book discusses both of these key aspects: the impact of algorithms, and the possibilities for future regulation.

Algorithms: Technology, Culture, Politics develops a relational, situated approach to algorithms. It takes a middle ground between theories that give the algorithm a singular and stable meaning in using it as a central analytic category for contemporary society and theories that dissolve the term into the details of empirical studies. The book discusses algorithms in relation to hardware and material conditions, code, data, and subjects such as users, programmers, but also "data doubles". The individual chapters bridge critical discussions on bias, exclusion, or responsibility with the necessary detail on the contemporary state of information technology.

In Algorithms of Oppression, Safiya Umoja Noble challenges the idea that search engines like Google offer an equal playing field for all forms of ideas, identities, and activities. Data discrimination is a real social problem; Noble argues that the combination of private interests in promoting certain sites, along with the monopoly status of a relatively small number of Internet search engines, leads to a biased set of search algorithms that privilege whiteness and discriminate against people of color, specifically women of color. Through an analysis of textual and media searches as well as extensive research on paid online advertising, Noble exposes a culture of racism and sexism in the way discoverability is created online.

T​his book critically explores how and to what extent artificial intelligence (AI) can infringe human rights and/or lead to socially harmful consequences and how to avoid these. The European Union has outlined how it will use big data, machine learning, and AI to tackle a number of inherently social problems, including poverty, climate change, social inequality and criminality. The contributors of this book argue that the developments in AI must take place in an appropriate legal and ethical framework and they make recommendations to ensure that harm and human rights violations are avoided.

The scope of Artificial Intelligence's (AI) hold on modern life is only just beginning to be fully understood. Academics, professionals, policymakers, and legislators are analysing the effects of AI in the legal realm, notably in human rights work. Artificial Intelligence technologies and modern human rights have lived parallel lives for the last sixty years, and they continue to evolve with one another as both fields take shape.Human Rights and Artificial Intelligence explores the effects of AI on both the concept of human rights and on specific topics, including civil and political rights, privacy, non-discrimination, fair procedure, and asylum. Second- and third-generation human rights are also addressed. By mapping this relationship, the book clarifies the benefits and risks for human rights as new AI applications are designed and deployed.Its granular perspective makes Human Rights and Artificial Intelligence a seminal text on the legal ramifications of machine learning.

Artificial intelligence technologies are spreading across all aspects of social life: from automated decision-making tools used by administrations to facial recognition, personal assistants, recruitment software, and medical diagnostic aids, no sector of activity escapes their deployment. While the benefits of these tools are undeniable, they also entail risks of errors, biases (related to gender, race, disability, etc.), discrimination, invisibility, and exclusion. The law can address these new ethical and socio-economic challenges. In different parts of the world, legislators are thus considering the need (or not) to create a legal framework and how to do so.

A software developer's misadventures in computer programming, machine learning, and artificial intelligence reveal why we should never assume technology always get it right. In Artificial Unintelligence, Meredith Broussard argues that our collective enthusiasm for applying computer technology to every aspect of life has resulted in a tremendous amount of poorly designed systems. We are so eager to do everything digitally--hiring, driving, paying bills, even choosing romantic partners--that we have stopped demanding that our technology actually work. Broussard, a software developer and journalist, reminds us that there are fundamental limits to what we can (and should) do with technology. With this book, she offers a guide to understanding the inner workings and outer limits of technology--and issues a warning that we should never assume that computers always get things right.

The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics-one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever "speak for themselves." Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.

This thought-provoking book follows the EU's journey into the digital age, explaining how it uses legislation and policy to tackle challenges such as the abuse of market power by Big Tech companies and the spread of hate speech and disinformation. Werner Stengg draws on his extensive experience in shaping digital policy to expertly analyse the EU's ambitious legislative and innovation programme, which focuses on human rights and prioritises trustworthy, transparent, and accountable usage of digital technologies. Alongside this examination of legislation and policy, Stengg also outlines the EU's major investment agenda into the digital infrastructures required to become a global player in our data-driven and AI-powered economy. Ultimately, the book highlights that innovations in the digital sphere are essential not only for the global competitiveness of European companies, but also for Europe to safeguard its resilience, autonomy, and technological sovereignty at a time of mounting geopolitical tensions.

"This unique volume presents AI in relation to ethical points of view in handling big data sets. Issues such as algorithmic biases, discrimination for specific patterns and privacy breaches may sometimes be skewed to affect research results so that certain fields to appear more appealing to funding agencies. The discussion on the ethics of AI is highly complex due to the involvement of many international stakeholders such as the UN, OECD, parliaments, industry groups, professional bodies, and individual companies. The issue of reliability is addressed including the emergence of synthetic life, 5G networks, intermingling of human artificial intelligence, nano-robots and cyber security tools"--

What will you do when your AI misbehaves? The promise of artificial intelligence is automated decision-making at scale, but that means AI also automates risk at scale. Are you prepared for that risk? Already, many companies have suffered real damage when their algorithms led to discriminatory, privacy-invading, and even deadly outcomes. Self-driving cars have hit pedestrians; HR algorithms have precluded women from job searches; mortgage systems have denied loans to qualified minorities. And often the companies who deployed the AI couldn't explain why the black box made the decision it did. In this environment, AI ethics isn't merely an academic curiosity, it's a business necessity. In "Ethical Machines," Reid Blackman gives you all you need to understand AI ethics as a risk management challenge.

This open access book begins with an algorithm-a set of IF...THEN rules used in the development of a new, ethical, video surveillance architecture for transport hubs. Readers are invited to follow the algorithm over three years, charting its everyday life. Questions of ethics, transparency, accountability and market value must be grasped by the algorithm in a series of ever more demanding forms of experimentation. Here the algorithm must prove its ability to get a grip on everyday life if it is to become an ordinary feature of the settings where it is being put to work.

Based on the latest insights from the cognitive sciences, economics, and public policy, Guardrails offers a novel approach to shaping decisions by embracing human agency in its social context. In this visionary book, Urs Gasser and Viktor Mayer-Schönberger show how the quick embrace of technological solutions can lead to results we don't always want, and they explain how society itself can provide guardrails more suited to the digital age, ones that empower individual choice while accounting for the social good, encourage flexibility in the face of changing circumstances, and ultimately help us to make better decisions as we tackle the most daunting problems of our times, such as global injustice and climate change. Whether we change jobs, buy a house, or quit smoking, thousands of decisions large and small shape our daily lives. Decisions drive our economies, seal the fate of democracies, create war or peace, and affect the well-being of our planet.

Artificial Intelligence was meant to be the great social equalizer that helps promote fairness by removing human bias from the equation, but is this true? Given that the policing and judicial systems can display human bias, this book explores how the technology they use can also reflect these prejudices. From healthcare services to social scoring in exams, to applying for and getting loans, AI outcomes often restrict those most in need of these services. Through personal stories from an esteemed Black Data Scientist and AI expert, this book attempts to demystify the algorithmic black box. AI pervades all aspects of modern society and affects everyone within it, yet its internal biases are rarely confronted.

This book is one of two companion volumes that explore theories and histories of information from a humanistic perspective. They consider information as a long-standing feature of social, cultural, and conceptual management, a matter of social practice, and a fundamental challenge for the humanities today. Bringing together essays by prominent critics, Information: Keywords highlights the humanistic nature of information practices and concepts by thinking through key terms. It describes and anticipates directions for how the humanities can contribute to our understanding of information from a range of theoretical, historical, and global perspectives.

Most of recorded human history is one big data gap. Starting with the theory of Mand the Hunter, the chroniclers of the past have left little space for women's role in the evolution of humanity, whether cultural or biological. Instead, the lives of men have been taken to represent those of humans overall. When it comes to the lives of the other half of humanity, there is often nothing but silence.

How did racism creep into the algorithms that govern our daily lives, from banking and shopping, to job applications? Connecting the legacy of enlightenment racism to forms of discrimination in modern day algorithms and Artificial Intelligence, this volume examines what data feeds into AI technology - and how this data will shape the future of humanity.

This book highlights research on the behavioral biases affecting judgmental accuracy in judgmental forecasting and showcases the state-of-the-art in judgment-based predictive analytics. In recent years, technological advancements have made it possible to use predictive analytics to exploit highly complex (big) data resources. Consequently, modern forecasting methodologies are based on sophisticated algorithms from the domain of machine learning and deep learning. However, research shows that in the majority of industry contexts, human judgment remains an indispensable component of the managerial forecasting process. This book discusses ways in which decision-makers can address human behavioral issues in judgmental forecasting.

This book provides an in-depth overview of what is currently happening in the field of Law and Artificial Intelligence (AI). From deep fakes and disinformation to killer robots, surgical robots, and AI lawmaking, the many and varied contributors to this volume discuss how AI could and should be regulated in the areas of public law, including constitutional law, human rights law, criminal law, and tax law, as well as areas of private law, including liability law, competition law, and consumer law. Aimed at an audience without a background in technology, this book covers how AI changes these areas of law as well as legal practice itself. This scholarship should prove of value to academics in several disciplines (e.g., law, ethics, sociology, politics, and public administration) and those who may find themselves confronted with AI in the course of their work, particularly people working within the legal domain (e.g., lawyers, judges, law enforcement officers, public prosecutors, lawmakers, and policy advisors).

Digitization, blockchain technology and Artificial Intelligence (AI) are fundamentally changing the fabric of societies, influencing lawmaking, legal scholarship and legal practice. The authors of this volume investigate the real-world developments that can be observed in this process, how established legal doctrines are being challenged, the regulatory issues societies face as a result, and how AI can be used in lawmaking and adjudication. By analyzing these four interrelated areas, the authors discuss conceptual issues of regulating AI, examine the impact of new technologies on commercial transactions and corporate governance, investigate civil liability rules for AI applications and explore key features and problems of digital dispute resolution. A recurring theme is that although "Law by Algorithm" might massively increase overall societal welfare, it runs the significant risk of benefitting only a few.

AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics - and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As thedebate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI.

This is the first accessible introduction to the political challenges related to AI. Using political philosophy as a unique lens through which to explore key debates in the area, the book shows how various political issues are already impacted by emerging AI technologies: from justice and discrimination to democracy and surveillance. Revealing the inherently political nature of technology, it offers a rich conceptual toolbox that can guide efforts to deal with the challenges raised by what turns out to be not only artificial intelligence but also artificial power.

AI promises to reduce unnecessary labour, speed up various forms of police work, encourage police forces to more efficiently apportion their resources, and enable police officers to prevent crime and protect people from a variety of future harms. However, the promises of predictive and AI technologies and innovations do not always match reality. They often have significant weaknesses, come at a considerable cost and require challenging trade- off s to be made. Focusing on the UK, the US and Australia, this book explores themes of choice architecture, decision- making, human rights, accountability and the rule of law, as well as future uses of AI and predictive technologies in various policing contexts. The text contributes to ongoing debates on the benefits and biases of predictive algorithms, big data sets, machine learning systems, and broader policing strategies and challenges.

This pioneering Research Handbook on Public Management and Artificial Intelligenceprovides a comprehensive overview of the potentials, challenges, and governance principles of AI in a public management context. Multidisciplinary in approach, it draws on a variety of jurisdictional perspectives and expertly analyses key topics relating to this socio-technical phenomenon. Showcasing contributions by a collection of eminent scholars from across the globe, this Research Handbook presents cutting-edge research on AI in public management. Organised into three parts corresponding with distinct foci of research, it explores the adoption and implementation of AI in public management settings, presents specific case studies and examples of AI in the public sector, and outlines future trends and directions in the evolution of AI adoption and use in public management.

Providing up-to-date coverage of the practical application of current modelling techniques in risk management, this real-world guide also explores new opportunities and challenges associated with implementing machine learning and artificial intelligence (AI) into the risk management process. Authors Terisa Roberts and Stephen Tonna provide readers with a clear understanding about the strengths and weaknesses of machine learning and AI while explaining how they can be applied to both everyday risk management problems and to evaluate the financial impact of extreme events such as global pandemics and changes in climate. Throughout the text, the authors clarify misconceptions about the use of machine learning and AI techniques using clear explanations while offering step-by-step advice for implementing the technologies into an organization's risk management model governance framework.

This SAGE Handbook brings together cutting edge social scientific research and theoretical insight into the emerging contours of digital society. Chapters explore the relationship between digitisation, social organisation and social transformation at both the macro and micro level, making this a valuable resource for postgraduate students and academics conducting research across the social sciences. The topics covered are impressively far-ranging and timely, including machine learning, social media, surveillance, misinformation, digital labour, and beyond. This innovative Handbook perfectly captures the state of the art of a field which is rapidly gaining cross-disciplinary interest and global importance, and establishes a thematic framework for future teaching and research.

Systemic Bias: Algorithms and Society looks at issues of computational bias in the contexts of cultural works, metaphors of magic and mathematics in tech culture, and workplace psychometrics. The output of computational models is directly tied not only to their inputs but to the relationships and assumptions embedded in their model design, many of which are of a social and cultural, rather than physical and mathematical, nature. How do human biases make their way into these data models, and what new strategies have been proposed to overcome bias in computed products?

Welcome to the dark side of Big Data. Tracing the arc of a person’s life, O’Neil exposes the black box models that shape our future, both as individuals and as a society. These “weapons of math destruction” score teachers and students, sort résumés, grant (or deny) loans, evaluate workers, target voters, set parole, and monitor our health. O’Neil calls on modelers to take more responsibility for their algorithms and on policy makers to regulate their use. But in the end, it’s up to us to become more savvy about the models that govern our lives.