Philosophy of dialectical materialism and logic.

This article extends property-owning democracy to the digital realm and introduces “data-owning democracy,” a new political economic regime characterized by the wide distribution of data as capital among citizens. Drawing on republican theory and acknowledging data's unique role in the digital economy, it proposes a two-tier model that combines different modes of data ownership and corresponding rights. The first layer of “data-owning democracy” is characterized by a digital public infrastructure that enables citizens to collectively generate (...) data and have a say in how their citizen data are used. In the second layer, individuals automatically receive machine-readable copies of their data whenever they are generated—a slightly more advanced form of the European Union's existing right to data portability (Art. 20). With its focus on empowerment, data-owning democracy is designed to be complementary to existing data protection regulations. It also illustrates how political theory more broadly, and republican theory specifically, can be instructive for specifying the normative components of a new political economy dealing with questions of empowerment and digital rights. (shrink)

The potential to collect brain data more directly, with higher resolution, and in greater amounts has heightened worries about mental and brain privacy. In order to manage the risks to individuals posed by these privacy challenges, some have suggested codifying new privacy rights, including a right to “mental privacy.” In this paper, we consider these arguments and conclude that while neurotechnologies do raise significant privacy concerns, such concerns are—at least for now—no different from those raised by other well-understood (...) class='Hi'>data collection technologies, such as gene sequencing tools and online surveillance. To better understand the privacy stakes of brain data, we suggest the use of a conceptual framework from information ethics, Helen Nissenbaum’s “contextual integrity” theory. To illustrate the importance of context, we examine neurotechnologies and the information flows they produce in three familiar contexts—healthcare and medical research, criminal justice, and consumer marketing. We argue that by emphasizing what is distinct about brain privacy issues, rather than what they share with other data privacy concerns, risks weakening broader efforts to enact more robust privacy law and policy. (shrink)

Robert John Ackermann deals decisively with the problem of relativism that has plagued post-empiricist philosophy of science. Recognizing that theory and data are mediated by data domains (bordered data sets produced by scientific instruments), he argues that the use of instruments breaks the dependency of observation on theory and thus creates a reasoned basis for scientific objectivity. Originally published in 1985. The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from (...) the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905. (shrink)

South Africa shared with the world the warning of a new strain of SARS-CoV2, Omicron, in November 2021. As a result, many high-income countries (HICs) instituted complete travel bans on persons leaving South Africa and other neighbouring countries. These bans were unnecessary from a scientific standpoint, and they ran counter to the International Health Regulations. In short, South Africa was penalized for sharing data. Data sharing during pandemics is commonly justified by appeals to solidarity. In this paper, we (...) argue that solidarity is, at best, an aspirational ideal to work toward but that it cannot ground an obligation to share data. Instead, low-and-middle income countries (LIMCs) should be guided by the principle of reciprocity, which states that we ought to return good for good received. Reciprocity is necessarily a conditional principle. LMICs, we argue, should only share data during future pandemics on the condition that HICs provide enforceable assurances that the benefits of data sharing will be equitably distributed and that LMICs won’t be penalized for sharing information. (shrink)

This article argues that the fact that an action will compound a prior injustice counts as a reason against doing the action. I call this reason The Anti-Compounding Injustice principle or aci. Compounding injustice and the aci principle are likely to be relevant when analyzing the moral issues raised by “big data” and its combination with the computational power of machine learning and artificial intelligence. Past injustice can infect the data used in algorithmic decisions in two distinct ways. (...) Sometimes prior injustice undermines the accuracy of the data itself. In these contexts, improving accuracy will also help to avoid compounding injustice. Other times, past injustice produces real-world differences among people with regard to skills, health, wealth, and other traits that employers, lenders, and others seek to measure. When decisions are based on accurate data that itself results from prior injustice, these decisions can also compound injustice. This second dynamic has received less attention than the first but is especially important because improving the accuracy of data will not mitigate this unfairness. (shrink)

In this paper, I present a version of a sense-data approach to perception, which differs to a certain extent from well-known versions like the one put forward by Jackson. I compare the sense-data view to the currently most popular alternative theories of perception, the so-called Theory of Appearing (a very specific form of disjunctivist approaches) on the one hand and reductive representationalist approaches on the other. I defend the sense-data approach on the basis that it improves substantially (...) on those alternative theories. (shrink)

Faced with the exponential development of Big Data and both its legal and economic repercussions, we are still slightly in the dark concerning the use of digital information. In the perpetual balance between confidentiality and transparency, this data will lead us to call into question how we understand certain paradigms, such as the Hippocratic Oath in medicine. As a consequence, a reflection on the study of the risks associated with the ethical issues surrounding the design and manipulation of (...) this "massive data" seems to be essential. This book provides a direction and ethical value to these significant volumes of data. It proposes an ethical analysis model and recommendations to better keep this data in check. This empirical and ethico-technical approach brings together the first aspects of a moral framework directed toward thought, conscience and the responsibility of citizens concerned by the use of data of a personal nature. Defines Big Data applications in health Presents the ethical value of the medical datasphere via the description of a model of an ethical analysis of Big Data Provides the recommendations and steps necessary for successful management and governance of personal health data Helps readers determine what conditions are essential for the development of the study of Big Data. (shrink)

Healthcare professionals decide wisely about personalized medicine, treatment plans, and resource allocation by utilizing big data analytics and machine learning. To guarantee that algorithmic recommendations are impartial and fair, however, ethical issues relating to prejudice and data privacy must be taken into account. Big data analytics and machine learning have a great potential to disrupt healthcare, and as these technologies continue to evolve, new opportunities to reform healthcare and enhance patient outcomes may arise. In order to investigate (...) the patient’s outcomes with empirical evidence, this research was conducted using an online survey to incorporate healthcare professionals, patient’s reviews, and clinical staff. The data were analyzed using SmartPLS 4.0 to predict the structural model. The findings revealed a direct impact as positive influence of using machine learning on healthcare performance and patient outcomes through big data analytics. Moreover, it is evident that this can lead to personalized treatment plans, early interventions, and improved patient outcomes. Additionally, big data analytics can help healthcare providers optimize resource allocation, improve operational efficiency, and reduce costs. The impact of big data analytics on patient outcome and healthcare performance is expected to continue to grow, making it an important area for investment and research. (shrink)

In “Brain Data in Context: Are New Rights the Way to Mental and Brain Privacy?” Daniel Susser and Laura Cabrera (2024) make a compelling case as to why the greater availability of neural data itsel...

Herbert Spencer, Victorian philosopher, biologist, sociologist and political theorist, one of the founders of Social Darwinism and author of the phrase 'survival of the fittest', was nominated for the Nobel Prize for Literature in 1902, losing out to Theodor Mommsen. Spencer left his post at The Economist in 1857 to focus on writing his ten-volume System of Synthetic Philosophy, a work that offers an ethics-based guide to human conduct to replace that provided by conventional religious belief. Published in 1879, this (...) volume seeks to demonstrate that social evolution tends towards greater individualism, altruism and co-operation. Spencer argues that it is possible to establish rules of right conduct on a scientific basis, and declares that this work is the culmination of his life's study. He was anxious to publish it outside the planned order of the System, because he feared that his death would prevent its completion. (shrink)

In response to calls to forget and unthink photography this article considers the computational environment and its consequences for the image making. What is there to know about images that are networked and generated with data processing techniques rather than with light and chemistry? How to analyse images and read what they are and what they represent, beyond what is visible in the picture? The article argues that glitches while aesthetically capturing errors in the machine point to broader conditions (...) of image making revealing systemic errors and not only its momentary technical failures. To explore digital images as affective phenomena to be sensed, the article looks at how glitch and its operations have been used by artists to generate digital images, and most importantly how they reveal computational systems as technical objects and affective infrastructures where discrimination is not an error but part of the system. (shrink)

We face reality presented with the data of conscious experience and nothing else. The project of early modern philosophy was to build a complete theory of the world from this starting point, with no cheating. Crucial to this starting point is the data of conscious sensory experience – sense data. Attempts to avoid this project often argue that the very idea of sense data is confused. But the sense-data way of talking, the sense-data language, (...) can be freed from every blemish using ideas from contemporary metaontology. We can adopt a sense-data framework that vindicates the traditional claims of sense-data theories and leads to plausible theories of perception and color. We can, we should, and in a sense, we must. Yet when we do, we face the traditional problem of external world skepticism, head-on. The real challenge of skepticism is forced upon us; it cannot honestly be avoided using externalist tricks, burden of proof shifting, or other razzle dazzle. But the challenge can be met: we are rational to posit the external world as the best explanation of the many synchronic and diachronic patterns over our sense-data. This paper argues for all of these points and ends with a plea for analytic empiricism – a traditional sense-data version of empiricism that uses all of the tools of analytic philosophy while avoiding the more questionable doctrines of the Vienna Circle (phenomenalism, verificationism). Analytic empiricism is our last best hope for completing the great philosophical project of early modern philosophy. (shrink)

Nowadays, protecting trust in social sciences also means engaging in open community dialogue, which helps to safeguard robustness and improve efficiency of research methods. The combination of open data, open review and open dialogue may sound simple but implementation in the real world will not be straightforward. However, in view of Begley and Ellis’s (2012) statement that, “the scientific process demands the highest standards of quality, ethics and rigour,” they are worth implementing. More importantly, they are feasible to work (...) on and likely will help to restore plausibility to social sciences research. Therefore, I feel it likely that the triplet of open data, open review and open dialogue will gradually emerge to become policy requirements regardless of the research funding source. (shrink)

Responsibilities of the data monitoring committee and motivating illustrations -- Composition of a data monitoring committee -- Independence of the data monitoring committee : avoiding conflicts of interest -- Confidentiality issues relating to the data monitoring committee -- Data monitoring committee meetings -- Data monitoring committee interactions with other trial components or related groups -- Statistical, philosophical and ethical issues in data monitoring -- Determining when a data monitoring committee is needed -- (...) Regulatory considerations for the operation of data monitoring committees -- Legal considerations for DMCs. (shrink)

This open access book explains how leading business organizations attempt to achieve the responsible and ethical use of artificial intelligence (AI) and other advanced information technologies. These technologies can produce tremendous insights and benefits. But they can also invade privacy, perpetuate bias, and otherwise injure people and society. To use these technologies successfully, organizations need to implement them responsibly and ethically. The question is: how to do this? Data ethics management, and this book, provide some answers. -/- The authors (...) interviewed and surveyed data ethics managers at leading companies. They asked why these experts see data ethics as important and how they seek to achieve it. This book conveys the results of that research on a concise, accessible way. -/- Much of the existing writing on data and AI ethics focuses either on macro-level ethical principles, or on micro-level product design and tooling. The interviews showed that companies need a third component: data ethics management. This third element consists of the management structures, processes, training and substantive benchmarks that companies use to operationalize their high-level ethical principles and to guide and hold accountable their developers. Data ethics management is the connective tissue makes ethical principles real. It is the focus of this book. -/- This book should be of use to organizations that wish to improve their own data ethics management efforts, legislators and policymakers who hope to build on existing management practices, scholars who study beyond compliance business behavior, and members of the public who want to understand better the threats that AI poses and how to reduce them. (shrink)

Data subject rights provide data controllers with obligations that can help with transparency, giving data subjects some control over their personal data. To date, a growing number of researchers have used these data subject rights as a methodology for data collection in research studies. No one, however, has gathered and analysed different academic research studies that use data subject rights as a methodology for data collection. To this end, we conducted a systematic (...) literature review that searched, compiled, and analysed 32 academic studies that use data subject rights as a data collection method. We find that the right of access is the most commonly-used data subject right by researchers, most studies are interested in measuring data subject rights compliance, and that a variety of difficulties exist in conducting research studies with data subject rights. We conclude that researchers should explore other data subject rights for alternative purposes, ease the process of exercising data subject rights, and improve the scalability of these studies. (shrink)

We have seen through many examples that data science and artificial intelligence can reinforce structural inequalities like sexism and racism. Data is power, and that power is distributed unequally. This book offers a vision for a feminist data science that can challenge power and work towards justice. This book takes a stand against a world that benefits some (including the authors, two white women) at the expense of others. It seeks to provide concrete steps for data (...) scientists seeking to learn how feminism can help them work towards justice, and for feminists seeking to learn how their own work can carry over to the growing field of data science. It is addressed to professionals in all fields where data-driven decisions are being made, as well as to communities that want to better understand the data that surrounds them. It is written for everyone who seeks to better understand the charts and statistics that they encounter in their day-to-day lives, and for everyone who seeks to better communicate the significance of such charts and statistics to others. This is an example-driven book written with a broad audience of scholars, students, and practitioners in mind. It offers a way of thinking about data, both their uses and their limits, that is informed by direct experience, by a commitment to action, and by the ideas associated with intersectional feminist thought. (shrink)

It is not easy to understand the history and origin of the different peoples of today's world inasmuch as scientific data are partial and seemingly contradictory. These roughly fall into three categories:-prehistoric data are remains of cultures and human skeletons. They allow us to affirm that such and such a region was inhabited in such and such an epoch. Their absence, however, means nothing, and they hardly permit the attribution of a biological origin to the peoples of the (...) past because of the rapid evolution of the forms and dimensions of the skeletons during the course of time;-present genetic data give an interesting picture of the biological relationship of peoples and of the way they diverged throughout history. Their interpretation is delicate, however, especially when it concerns small populations;-“biometric” data involve the measurement of pigmentation, dimensions and proportions of the body and organs. They are susceptible to very rapid variations during the course of history and mainly give an indication of the adaptation of the people to a certain kind of environment: tropical, temperate, cold or arctic, plain or altitude, forest, savannah, prairie, steppe or desert. (shrink)

Within the body of Star Trek television series and movies, the concept of personhood stands out in one particular episode that may just be the best episode ever. Its title, “The Measure of a Man”, evokes the famous saying by Greek pre‐Socratic philosopher Protagoras, who claimed, “Man is the measure of all things”. The moral personhood of both Pinocchio and Data really comes from within rather than because of a legal decision: they both learn to be brave, truthful and (...) unselfish, and with conscience as their guide, a moral measure of a human. Immanuel Kant suggests that a truly moral action is one based on a principle that one can frame as a universal moral principle for everyone to follow, every truly moral action implies a duty. Kant anticipated Data's situation, arguing that any rational being should be treated as an end in himself or herself. (shrink)

Investigations of data-centered efforts in advocacy and activism are often cast in terms of a narrative of opposition between grassroots activists working through and with data, and corporations or institutions whose actions data might expose. The boundaries are, however, not so distinct in practice. Indeed, one outcome of successful advocacy efforts for opening big data to the public is that the activists may find themselves drawn into the institutions they critique or view as impediments in order (...) to actualize those efforts from the inside. Drawing on an ongoing ethnographic investigation of urban data initiatives inside city government, we explore the productive ambivalences of activism and advocacy that arise when those with data activist ideals find themselves operating within the government structure. (shrink)

We critically engage two traditional views of scientific data and outline a novel philosophical view that we call the pragmatic-representational view of data. On the PR view, data are representations that are the product of a process of inquiry, and they should be evaluated in terms of their adequacy or fitness for particular purposes. Some important implications of the PR view for data assessment, related to misrepresentation, context-sensitivity, and complementary use, are highlighted. The PR view provides (...) insight into the common but little-discussed practices of iteratively reusing and repurposing data, which result in many datasets’ having a phylogeny—an origin and complex evolutionary history—that is relevant to their evaluation and future use. We relate these insights to the open-data and data-rescue movements, and highlight several future avenues of research that build on the PR view of data. (shrink)

A significant theory of corporate social responsibility is the Stakeholder Model. Within this model, entities make decisions that impact all stakeholders. Occasionally, the decision that is made ultimately impacts one stakeholder differently than another. Negative data by its very definition is seen as problematic for any organization as it pertains to its stakeholders. When confronted with the data or the potential of the data being negative to desired outcomes or directions of programs, an organization’s leadership may be (...) faced with an ethical conundrum. Should the data be included and shown to all stakeholders? Or should the data be buried and avoided even to the point of engaging in questionable conduct in the management of paperwork and record keeping? (shrink)

This article presents and discusses different kinds of data documents, including data sets, data studies, data papers and data journals. It provides descriptive and bibliometric data on different kinds of data documents and discusses the theoretical and philosophical problems by classifying documents according to the DIKW model (data documents, information documents, knowl­edge documents and wisdom documents). Data documents are, on the one hand, an established category today, even with its own (...) class='Hi'>data citation index (DCI). On the other hand, data documents have blurred boundaries in relation to other kinds of documents and seem sometimes to be understood from the problematic philosophical assumption that a datum can be understood as “a single, fixed truth, valid for everyone, everywhere, at all times”. (shrink)

There is no doubt science is currently suffering from a credibility crisis. This thought-provoking book argues that, ironically, science's credibility is being undermined by tools created by scientists themselves. Scientific disinformation and damaging conspiracy theories are rife because of the internet that science created, the scientific demand for empirical evidence and statistical significance leads to data torturing and confirmation bias, and data mining is fuelled by the technological advances in Big Data and the development of ever-increasingly powerfulcomputers. (...) Using a wide range of entertaining examples, this fascinating book examines the impacts of society's growing distrust of science, and ultimately provides constructive suggestions for restoring the credibility of the scientific community. (shrink)

While the term “reactivity” has come to be associated with specific phenomena in the social sciences, having to do with subjects’ awareness of being studied, this paper takes a broader stance on this concept. I argue that reactivity is a ubiquitous feature of the psychological subject matter and that this fact is a precondition of experimental research, while also posing potential problems for the experimenter. The latter are connected to the worry about distorted data and experimental artifacts. But what (...) are experimental artifacts and what is the most productive way of dealing with them? In this paper, I approach these questions by exploring the ways in which experimenters in psychology simultaneously exploit and suppress the reactivity of their subject matter in order to produce experimental data that speak to the question or subject matter at hand. Highlighting the artificiality of experimental data, I raise the question of what distinguishes a genuine experimental result from an experimental artifact. My analysis construes experimental results as the outcomes of inferences from the data that take material background assumptions as auxiliary premises. Artifacts occur when one or more of these background assumptions are false, such that the data do not reliably serve the purposes they were generated for. I conclude by laying out the ways in which my analysis of data quality is relevant to, and informed by, recent debates about the replicability of experimental results. (shrink)

Public sector adoption of AI techniques in welfare systems recasts historic national data as resource for machine learning. In this paper, we examine how the use of register data for development of predictive models produces new ‘afterlives’ for citizen data. First, we document a Danish research project’s practical efforts to develop an algorithmic decision-support model for social workers to classify children’s risk of maltreatment. Second, we outline the tensions emerging from project members’ negotiations about which datasets to (...) include. Third, we identify three types of afterlives for citizen data in machine learning projects: (1) data afterlives for training and testing the algorithm, acting as ‘ground truth’ for inferring futures, (2) data afterlives for validating the algorithmic model, acting as markers of robustness, and (3) data afterlives for improving the model’s fairness, valuated for reasons of data ethics. We conclude by discussing how, on one hand, these afterlives engender new ethical relations between state and citizens; and how they, on the other hand, also articulate an alternative view on the value of datasets, posing interesting contrasts between machine learning projects developed within the context of the Danish welfare state and mainstream corporate AI discourses of the bigger, the better. (shrink)

Between 2006 and 2011, humanity accumulated 1,600 EB of data. As a result of this growth, there is now more data produced than available storage. This article explores the problem of “Big Data,” arguing for an epistemological approach as a possible solution to this ever-increasing challenge.

The potential to collect brain data more directly, with higher resolution, and in greater amounts has heightened worries about mental and brain privacy. In order to manage the risks to individuals posed by these privacy challenges, some have suggested codifying new privacy rights, including a right to “mental privacy.” In this paper, we consider these arguments and conclude that while neurotechnologies do raise significant privacy concerns, such concerns are—at least for now—no different from those raised by other well-understood (...) class='Hi'>data collection technologies, such as gene sequencing tools and online surveillance. To better understand the privacy stakes of brain data, we suggest the use of a conceptual framework from information ethics, Helen Nissenbaum’s “contextual integrity” theory. To illustrate the importance of context, we examine neurotechnologies and the information flows they produce in three familiar contexts—healthcare and medical research, criminal justice, and consumer marketing. We argue that by emphasizing what is distinct about brain privacy issues, rather than what they share with other data privacy concerns, risks weakening broader efforts to enact more robust privacy law and policy. (shrink)

Data do not belong to predictive analytics only. Neither do concepts belong to theoretical modeling only. This talk will explore and question the changing relationships between data and concepts in models today, especially in the case of multiscale models. It will show that there are different types of integrative models, and that some are new. These new integrative models take their part in a cycling methodology of modeling where measures, estimations, reconstructions, simulations, concept-driven models and mathematics interact and (...) take their part too. (shrink)

Combining information from two or possibly several blocks of data is gaining increased attention and importance in several areas of science and industry. Typical examples can be found in chemistry, spectroscopy, metabolomics, genomics, systems biology and sensory science. Many methods and procedures have been proposed and used in practice. The area goes under different names: data integration, data fusion, multiblock analyses, multiset analyses and a few more. This book is an attempt to give an up-to-date treatment of (...) the most used and important methods within an important branch of the area; namely methods based on so-called components or latent variables. These methods have already obtained an enormous attention in for instance chemometrics, bioinformatics, machine learning and sensometrics and have proved to be important both for prediction and interpretation. The book is primarily a description of methodologies, but most of the methods will be illustrated by examples from the above-mentioned areas. The book is written such that both users of the methods as well as method developers will hopefully find sections of interest. In the end of the book there is a description of a software package developed particularly for the book. This package is freely available in R and covers many of the methods discussed. To distinguish the different type of methods from each other, the book is divided into five parts. Part I is introduction and preliminary concepts. Part II is the core of the book containing the main unsupervised and supervised methods. Part III deals with more complex structures and, finally, Part IV discusses alternative unsupervised and supervised methods. The book ends with Part V discussing the available software. Our recommendation for reading the book are as follows. A minimum read of the book would involve chapters 1, 2, 3, 5 and 7. Chapters 4, 6 and 8 are more specialized and chapters 9 and 10 contain methods we think are more advanced or less obvious to use. We feel privileged to have so many friendly colleagues who were willing to spend their time on helping us to improve the book by reading separate chapters. We would like to express our thanks to: Rasmus Bro, Margriet Hendriks, Ulf Indahl, Henk Kiers, Ingrid MaÌ⁽ge, Federico Marini, AÌ⁽smund Rinnan, Rosaria Romano, Lars Erik Solberg, Marieke Timmerman, Oliver Tomic, Johan Westerhuis and Barry Wise. Of course, the correctness of the final text is fully our responsibility! (shrink)

In this amazingly prophetic work, done late in his career, Herbert Spencer offers an approach to ethics that anticipates developments throughout the twentieth century. He moves away from the twin evils of ethical doctrines bequeathed to us by an ancient past that are simply no longer feasible but also avoids modern standards of ethical conduct that are simply impossible to attain. "By association with rules that cannot be obeyed," Spencer writes, "rules that can be obeyed lose their authority." The volume (...) opens with three chapters on conduct: its evolution, good and bad, and ways of judgment. This is followed by a series of chapters that examine ethics from a variety of scientific perspectives: physics, biology, psychology, and sociology. The work then moves on to specific issues of deep human concern: the relativity of pleasures and pain, egoism versus altruism in explaining actions, and trial and compromise in decision-making about ethical concerns. Spencer's work anticipates the movement toward pragmatic, naturalistic, and even positivist approaches to ethics. He emphasizes that a relativist approach while in keeping with the spirit of the industrial age, also poses a variety of problems that admit only of empirical solutions. He understands that his critical stance on absolutism should not blind researchers to the ideals assumed by the ancients that assist people in their everyday living. In short, this is a remarkable work, entirely modern, and yet containing a sharp evaluation of how ethical data serve to enhance ethical conduct. (shrink)

This paper provides a restatement and defense of the data/ phenomena distinction introduced by Jim Bogen and me several decades ago (e.g., Bogen and Woodward, The Philosophical Review, 303–352, 1988). Additional motivation for the distinction is introduced, ideas surrounding the distinction are clarified, and an attempt is made to respond to several criticisms.

This article provides a history of private sector tracking technologies, examining how the advent of commercial surveillance centered around a logic of data capitalism. Data capitalism is a system in which the commoditization of our data enables an asymmetric redistribution of power that is weighted toward the actors who have access and the capability to make sense of information. It is enacted through capitalism and justified by the association of networked technologies with the political and social benefits (...) of online community, drawing upon narratives that foreground the social and political benefits of networked technologies. I examine its origins in the wake of the dotcom bubble, when technology makers sought to develop a new business model to support online commerce. By leveraging user data for advertising purposes, they contributed to an information environment in which every action leaves behind traces collected by companies for commercial purposes. Through analysis of primary source materials produced by technology makers, journalists, and business analysts, I examine the emergence of data capitalism between the mid-1990s and mid-2000s and its central role in the contemporary information economy. (shrink)

ABSTRACT:Technological advances introduce the possibility that, in the future, firms will be able to use big-data analysis to discover and offer consumers their individual reservation price. This can generate some interesting benefits, such as a better state of affairs in terms of equality of both welfare and resources, as well as increased social welfare. However, these benefits are countered by considerations of relational equality. This article takes up the market-failures approach as its basis to demonstrate what is wrong with (...) using big data to personalize prices. The article offers an improvement to the market-failures approach and argues that what is wrong with using big data to personalize prices is that it unfairly undermines consumers’ ability to benefit from the market, which is the very point of having a market. (shrink)