Philosophy of dialectical materialism and logic.

Indikatoren sind Anzeiger, die zur Steuerung verwendet werden. Als Anzeiger dienen sie dazu, Auskunft über Gegenstände oder Sachverhalte zu geben, die nicht, schwer oder nur unter großem Aufwand messbar sind. In der Regel werden quantitative Maßzahlen erstellt, die entsprechend dem eigentlich interessierenden Objekt interpretiert werden (Kenngrößen).

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)

The first few years of the 21st century were characterised by a progressive loss of privacy. Two phenomena converged to give rise to the data economy: the realisation that data trails from users interacting with technology could be used to develop personalised advertising, and a concern for security that led authorities to use such personal data for the purposes of intelligence and policing. In contrast to the early days of the data economy and internet surveillance, the (...) last few years have witnessed a rising concern for privacy. As bad data practices have come to light, citizens are starting to understand the real cost of using online digital technologies. Two events stamped 2018 as a landmark year for privacy: the Cambridge Analytica scandal, and the implementation of the European Union’s General Data Protection Regulation (GDPR). The former showed the extent to which personal data has been shared without data subjects’ knowledge and consent and many times for unacceptable purposes, such as swaying elections. The latter inaugurated the beginning of robust data protection regulation in the digital age. Getting privacy right is one of the biggest challenges of this new decade of the 21st century. The past year has shown that there is still much work to be done on privacy to tame the darkest aspects of the data economy. As data scandals continue to emerge, questions abound as to how to interpret and enforce regulation, how to design new and better laws, how to complement regulation with better ethics, and how to find technical solutions to data problems. The aim of the research project Data, Privacy, and the Individual is to contribute to a better understanding of the ethics of privacy and of differential privacy. The outcomes of the project are seven research papers on privacy, a survey, and this final report, which summarises each research paper, and goes on to offer a set of reflections and recommendations to implement best practices regarding privacy. (shrink)

Data science, and the related field of big data, is an emerging discipline involving the analysis of data to solve problems and develop insights. This rapidly growing domain promises many benefits to both consumers and businesses. However, the use of big data analytics can also introduce many ethical concerns, stemming from, for example, the possible loss of privacy or the harming of a sub-category of the population via a classification algorithm. To help address these potential ethical (...) challenges, this paper maps and describes the main ethical themes that were identified via systematic literature review. It then identifies a possible structure to integrate these themes within a data science project, thus helping to provide some structure in the on-going debate with respect to the possible ethical situations that can arise when using data science analytics. (shrink)

This theme issue has the founding ambition of landscaping Data Ethics as a new branch of ethics that studies and evaluates moral problems related to data (including generation, recording, curation, processing, dissemination, sharing, and use), algorithms (including AI, artificial agents, machine learning, and robots), and corresponding practices (including responsible innovation, programming, hacking, and professional codes), in order to formulate and support morally good solutions (e.g. right conducts or right values). Data Ethics builds on the foundation provided by (...) Computer and Information Ethics but, at the same time, it refines the approach endorsed so far in this research field, by shifting the Level of Abstraction of ethical enquiries, from being information-centric to being data-centric. This shift brings into focus the different moral dimensions of all kinds of data, even the data that never translate directly into information but can be used to support actions or generate behaviours, for example. It highlights the need for ethical analyses to concentrate on the content and nature of computational operations — the interactions among hardware, software, and data — rather than on the variety of digital technologies that enables them. And it emphasises the complexity of the ethical challenges posed by Data Science. Because of such complexity, Data Ethics should be developed from the start as a macroethics, that is, as an overall framework that avoids narrow, ad hoc approaches and addresses the ethical impact and implications of Data Science and its applications within a consistent, holistic, and inclusive framework. Only as a macroethics Data Ethics will provide the solutions that can maximise the value of Data Science for our societies, for all of us, and for our environments. (shrink)

Als Vorstellung eines vom Wissen der Medizin abweichenden Behandlungsfalls können Fallberichte darauf Einfluss haben, welche Maßnahmen zukünftig als medizinisch indiziert gelten. Die öffentliche Präsentation der getroffenen Handlungen und Empfehlungen liegt in der Regel ausschließlich in der Hand ärztlicher Autor*innen – Betroffene kommen hingegen kaum zu Wort. Während in der klinischen Patientenversorgung bereits hohes Gewicht auf Patientenperspektiven und -erzählungen gelegt wird, scheinen sich diese Entwicklungen im Fallbericht als Form wissenschaftlicher Kommunikation so nicht widerzuspiegeln. Vor diesem Hintergrund wird im Folgenden aus gattungstheoretischer (...) Perspektive untersucht, welche Rolle Patientenperspektiven und -erzählungen nicht nur – wie in der bisherigen Forschung vor allem untersucht – im Bereich des Klinischen, sondern gerade auch des Kasuistischen und den damit verbundenen Fragen klinischer Indikationsreflexion und -empfehlung zukommen sollten. Dabei wird nicht zuletzt argumentiert, dass Narrationen analog zu Indikationsstellungen gerade ein Moment des Ungesicherten und Singulären betonen, das für die Textsorte des Fallberichts und daraus abgeleiteten Indikationsüberlegungen von spezifischem Wert ist. (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)

Moving away from the strong body of critique of pervasive ‘bad data’ practices by both governments and private actors in the globalized digital economy, this book aims to paint an alternative, more optimistic but still pragmatic picture of the datafied future. The authors examine and propose ‘good data’ practices, values and principles from an interdisciplinary, international perspective. From ideas of data sovereignty and justice, to manifestos for change and calls for activism, this collection opens a multifaceted conversation (...) on the kinds of futures we want to see, and presents concrete steps on how we can start realizing good data in practice. (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)

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)

Recently, it has been argued that the use of Big Data transforms the sciences, making data-driven research possible and studying causality redundant. In this paper, I focus on the claim on causal knowledge by examining the Big Data project EXPOsOMICS, whose research is funded by the European Commission and considered capable of improving our understanding of the relation between exposure and disease. While EXPOsOMICS may seem the perfect exemplification of the data-driven view, I show how causal (...) knowledge is necessary for the project, both as a source for handling complexity and as an output for meeting the project’s goals. Consequently, I argue that data-driven claims about causality are fundamentally flawed and causal knowledge should be considered a necessary aspect of Big Data science. In addition, I present the consequences of this result on other data-driven claims, concerning the role of theoretical considerations. I argue that the importance of causal knowledge and other kinds of theoretical engagement in EXPOsOMICS undermine theory-free accounts and suggest alternative ways of framing science based on Big Data. (shrink)

This paper presents an ethical framework designed to support the development of critical data literacy for research methods courses and data training programmes in higher education. The framework we present draws upon our reviews of literature, course syllabi and existing frameworks on data ethics. For this research we reviewed 250 research methods syllabi from across the disciplines, as well as 80 syllabi from data science programmes to understand how or if data ethics was taught. We (...) also reviewed 12 data ethics frameworks drawn from different sectors. Finally, we reviewed an extensive and diverse body of literature about data practices, research ethics, data ethics and critical data literacy, in order to develop a transversal model that can be adopted across higher education. To promote and support ethical approaches to the collection and use of data, ethics training must go beyond securing informed consent to enable a critical understanding of the techno-centric environment and the intersecting hierarchies of power embedded in technology and data. By fostering ethics as a method, educators can enable research that protects vulnerable groups and empower communities. (shrink)

Parts I through III of this paper will examine several, increasingly comprehensive forms of aggregation, ranging from insurance reimbursement “lock-in” programs to PDMPs to completely unified electronic medical records. Each part will advocate for the adoption of these aggregation systems and provide suggestions for effective implementation in the fight against opioid misuse. All PDMPs are not made equal, however, and Part II will, therefore, focus on several elements — mandating prescriber usage, streamlining the user interface, ensuring timely data uploads, (...) creating a national data repository, mitigating privacy concerns, and training doctors on how to respond to perceived doctor-shopping — that can make these systems more effective. In each part, we will also discuss the privacy concerns of aggregating data, ranging from minimal to significant, and highlight the unique role of stigma in motivating these concerns. In Part IV, we will conclude by suggesting remedial steps to offset this loss of privacy and to combat the stigma around SUDs and mental health disorders in general. (shrink)

The HRAF databases, eHRAF World Cultures and eHRAF Archaeology, each containing large corpora of curated text subject-indexed at the paragraph-level by anthropologists, were designed to facilitate rapid retrieval of information. The texts describe social and cultural life in past and present societies around the world. As of the spring of 2018, eHRAF contains almost three million indexed “paragraph” units from over 8000 documents describing over 400 societies and archaeological traditions. This chapter first discusses concrete problems of scale resulting from large (...) numbers of complex elements retrieved by any given search. Second, we discuss potential and partial solutions that resolve these problems to advance research, whether based on specific hypotheses, classification or identifying and evaluating embedded patterns of relationships. Third, we discuss new kinds of research possibilities that can be further advanced, have not yet been successfully attempted, or have not even been considered using anthropological data because of scale and complexity of achieving a result. (shrink)

Data exclusivity is a temporary exclusive user right on the clinical data that need to be submitted to the regulatory authorities to prove that a new drug is safe and effective. For the pharmaceutical industry, data exclusivity is an important addition to the patent system, as data exclusivity will de facto delay the market entry of generic drugs until after the exclusive user rights on the clinical data have expired. In order to assess the normative (...) legitimacy of the industry’s demand to include minimum standards for data exclusivity in international agreements, this chapter evaluates the three justifications that have frequently been advanced, depicting data exclusivity as an essential policy tool to promote innovation, a mechanism to prevent the generic industry from ‘free-riding,’ and a legal instrument to protect the industry’s property rights in clinical data. (shrink)

This chapter contains sections titled: Introduction Misuse of Data Misuse of Research Funds.

We live in the Information Age. Advances over the past 50 years in computing technology have enabled ever-expanding capacity to generate, store, transfer, process and analyse information about people, societies, products, services, the environment—nearly every aspect of the world. In parallel, concerns over how such data is being used have emerged, focusing especially on issues of privacy and confidentiality. Yet as technological capabilities continue to expand, the debate over ethical uses of data inevitably has evolved as well. Whereas (...) we used to just talk about data, now there is great concern over Big Data. This chapter explores what, exactly, Big Data is; the ethical ramifications of its emergence, particularly for consent and anonymization; and what sorts of policy solutions are being proposed to address these emerging challenges. We will not defend one particular approach, but hope this chapter can be a useful reference for those who are contemplating difficult questions surrounding responsible management of Big Data. (shrink)

Ontological realism aims at the development of high quality ontologies that faithfully represent what is general in reality and to use these ontologies to render heterogeneous data collections comparable. To achieve this second goal for clinical research datasets presupposes not merely (1) that the requisite ontologies already exist, but also (2) that the datasets in question are faithful to reality in the dual sense that (a) they denote only particulars and relationships between particulars that do in fact exist and (...) (b) they do this in terms of the types and type-level relationships described in these ontologies. While much attention has been devoted to (1), work on (2), which is the topic of this paper, is comparatively rare. Using Referent Tracking as basis, we describe a technical data wrangling strategy which consists in creating for each dataset a template that, when applied to each particular record in the dataset, leads to the generation of a collection of Referent Tracking Tuples (RTT) built out of unique identifiers for the entities described by means of the data items in the record. The proposed strategy is based on (i) the distinction between data and what data are about, and (ii) the explicit descriptions of portions of reality which RTTs provide and which range not only over the particulars described by data items in a dataset, but also over these data items themselves. This last feature allows us to describe particulars that are only implicitly referred to by the dataset; to provide information about correspondences between data items in a dataset; and to assert which data items are unjustifiably or redundantly present in or absent from the dataset. The approach has been tested on a dataset collected from patients seeking treatment for orofacial pain at two German universities and made available for the NIDCR-funded OPMQoL project. (shrink)

ICTs, personal data, digital rights, the GDPR, data privacy, online security… these terms, and the concepts behind them, are increasingly common in our lives. Some of us may be familiar with them, but others are less aware of the growing role of ICTs and data in our lives - and the potential risks this creates. These risks are even more pronounced for vulnerable groups in society. People can be vulnerable in different, often overlapping, ways, which place them (...) at a disadvantage to the majority of citizens; Table 3 in this guide presents some of the many forms and causes of vulnerability. As a result, vulnerable people need greater support to navigate the digital world, and to ensure that they are able to exercise their rights. This guide explains where such support can be found, and also answers the following questions: - What are the main ethical and legal issues around ICTs for vulnerable citizens? - Who is vulnerable in Europe? - How do issues around ICTs affect vulnerable people in particular? This guide is a resource for members of vulnerable groups, people who work with vulnerable groups, and citizens more broadly. It is also useful for data controllers1 who collect data about vulnerable citizens. While focused on citizens in Europe, it may be of interest to people in other parts of the world. It forms part of the Citizens’ Information Pack produced by the PANELFIT project, and is available in English, French, German, Italian and Spanish. You are welcome to translate this guide into other languages. Please send us a link to online versions in other languages, so that we can add them to the project website. (shrink)

Fremdes in Anatolien: Importgüter aus dem Ostmittelmeerraum und Mesopotamien als Indikator für spätbronzezeitliche Handels- und Kulturkontakte. By Ekin Kozal. Schriften zur Vorderasiatischen Archäologie, vol. 11. Wiesbaden: Harrassowitz Verlag, 2017. Pp. 261, illus. €84.

Die Möglichkeiten der Reproduktionsmedizin erweitern sich ständig. Bei einigen Maßnahmen assistierter Reproduktion ist es oft unklar, ob eine Indikation vorliegt oder ob diese Maßnahmen als wunscherfüllend anzusehen sind. Die Unterscheidung zwischen medizinisch indizierter Maßnahme und wunscherfüllender Behandlung hängt von dem hier verwendeten Konzept der Indikation ab. Daher kommt dem Konzept der Indikation auf dem Gebiet der Reproduktionsmedizin ein besonderer Stellenwert zu. Dabei fällt auf, dass die Abgrenzung zwischen medizinisch indizierter Behandlung und Wunschbehandlung nicht allein klinisch begründet ist, sondern implizit oder (...) explizit normative Urteile enthält. Der sprachlich-kommunikative Aspekt spielt hierbei eine zentrale Rolle. Der übliche Sprachgebrauch von „indiziert“ suggeriert ein Urteil, das allein auf empirisch-klinischen Fakten basiert, und verdeckt die normativen Urteile, die in das Konzept der Indikation einfließen. Ziel des vorliegenden Beitrags ist es, den normativen Gehalt der Indikation in der Reproduktionsmedizin zu analysieren. Grundlage ist die eingehende Betrachtung des Konzepts der Indikation und der besonderen Stellung der Indikation in der Reproduktionsmedizin. Aus dieser Betrachtung ergibt sich, dass der normative Gehalt der Indikation von einem soziokulturellen Skript bezüglich Elternschaft geprägt ist, das sich v. a. im Leistungsrecht niederschlägt. Dieses Skript enthält gesellschaftliche Anschauungen, Normen und Rollenbilder und kann als „gute Elternschaft“ bezeichnet werden. Das Skript der guten Elternschaft wirkt als Regulativ, das durch leistungsrechtliche Vorgaben über den Zugang zu reproduktionsmedizinischen Maßnahmen bestimmt. Es basiert auf traditionellen heteronormativen Ehe- und Familienkonzepten und sieht Elternschaft für heterosexuelle, verheiratete Paare vor. Somit ergibt sich der Widerspruch, dass Verfahren der Reproduktionsmedizin, die den Raum für die Fortpflanzung jenseits traditionell-heteronormativer Konzepte öffnen, von ebenjenen Konzepten legitimiert und limitiert werden. (shrink)

Purpose This study advances a reconceptualization of data and information which overcomes normative understandings often contained in data policies at national and international levels. This study aims to propose a conceptual framework that moves beyond subject- and collective-centric normative understandings. Design/methodology/approach To do so, this study discusses the European Union (EU) and China’s approaches to data-driven technologies highlighting their similarities and differences when it comes to the vision underpinning how tech innovation is shaped. Findings Regardless of the (...) different attention to the subject (the EU) and the collective (China), the normative understandings of technology by both actors remain trapped into a positivist approach that overlooks all that is not and cannot be turned into data, thus hindering the elaboration of a more holistic ecological thinking merging humans and technologies. Originality/value Revising the philosophical and political debate on data and data-driven technologies, a third way is elaborated, i.e. federated data as commons. This third way puts the subject as part by default of a collective at the centre of discussion. This framing can serve as the basis for elaborating sociotechnical alternatives when it comes to define and regulate the mash-up of humans and technology. (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)

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)

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.

A discussion of lessons learned in the first months of the COVID-19 pandemic which allowed data safety monitoring boards (DSMBs) to continue their work protecting the interests of human research participants while preserving research studies.