Issue 024: Ethics of Big Data
edited by Klaus Wiegerling, Michael Nerurkar, Christian Wadephul
Does ‚big‘ in the notion of Big Data also imply ’new‘ – a qualitative ’new‘? Is ‚Big Data‘ a legitimate new area of dedicated ethical analysis or do the rules of the classical ‚Ethics of Data‘ simply apply to the ‚Ethics of Big Data‘ as well?
Hegel has argued in his ‚Logic‘ that from a certain point quantitative change leads to a qualitative shift. The prime example is water heated up vaporizing at 100 degrees centigrade thus becoming steam. Without indulging into the discussion of Hegel’s concept especially not in the dialectic reception of Marx the question remains: is or from which point on is ‚big‘ also ’new‘? For a first approach let’s see how big ‚Big Data‘ is:
- In 1995 the world wide web was considered to contain some 30 GB of data. That would fit on a regular USB stick today.
- In 2007 the total amount of data stored was calculated to 295 exa bytes. If we were to take all that information and store it in books, we could cover the entire area of the US or China in 13 layers of books.
- Facebook today stores, accesses, and analyses some 30+ Petabytes of user generated data i.e. thousand times more data than stored in the U.S. Library of Congress
- And the amount of data generated will continue to double every 2 years leading to a production of 40 zeta bytes of new data per year in 2020. If there was a star for every byte of data there would be a galaxy of data for every sand corn on the planet by then.
This technological development grows faster than everything else humanity has faced before and its social impact will be massive as well as pervasive reaching deeply into our daily life. So let us not investigate if the Hegelian concept applies. Let us analyze thoroughly how we shall handle this significant phenomenon from an ethical perspective. The editors of this issue have drawn together some very good suggestions. We thank them for thus contributing not only to the vast ocean of data produced in 2016 but also to the very discussion of its ethical relevance for us. We hope you appreciate it.
Ethics of Big Data: Introduction
by Michael Nerurkar, Christian Wadephul, Klaus Wiegerling
Datavisions – On Panoptica, Oligoptica, and (Big) Data
by Regine Buschauer
Abstract: In focusing on relations between data and vision and proposing to address big data in terms of currently dominant optical metaphors (and, quite literally, in terms of ‘visions’), the paper makes a case for an approach that allows for clearer distinctions between big data as ‘visions’, and data technologies. (Re)assessing (present and past) notions and visions of panoptic data technologies, I outline three perspectives on the nexus between data and vision(s). Following Bruno Latour’s counter-image of “oligoptica”, I argue, more generally, in favour of a conceptual framework that understands big data as a sociotechnical infrastructure, and discuss, drawing on more recent studies, in which ways this approach allows to address social and ethical implications of present data technologies and practices in a more differentiated way.
Big Data for a Fairer Democracy?
by Jessica Heesen
Abstract: Big data-analysis is linked to the expectation to provide a general image of socially relevant topics and processes. Similar to this, the idea of the public sphere involves being representative of all citizens and of important topics and problems. This contribution, on one side, aims to explain how a normative concept of the public sphere could be infiltrated by big data. On the other, it will discuss how participative processes and common wealth can profit from a thorough use of big data analysis. As important parts of the argument, two concepts will be introduced: the numerical public (as a public that is constituted by machine-communication) and total politicisation (as a loss of negative freedom of expression).
The Role of Big Data in Ambient Assisted Living
by Arne Manzeschke, Galia Assadi and Willy Viehöver
Abstract: Big Data and biopolitics are two major issues currently attracting attention in public health discourse, but also in sociology of knowledge, STS Studies as well as in philosophy of science and bioethics. The paper considers big data to be a new form and instrument of biopolitics (Foucault) which addresses both the categories of body and space. It is expected to fundamentally transform health care systems, domestic environments and practices of self-observation and reflection. Accordingly the paper points out some problems and pitfalls as well as open questions that have emerged in the field of AAL, which merit more attention in future public and academic debate.
Data Analytics as Predictor of Character or Virtues, and the Risks to Autonomy
by Harald Weston
Abstract: Can we measure and predict character with predictive analytics so a business can better assess, ideally objectively, whether to lend money or extend credit to that person, beyond current objective measures of credit scores (when available) and standard financial metrics like solvency and debt ratios? We and the analysts probably do not know enough about character to try to measure it, though it might be more useful to measure and predict a person’s temperance and prudence as virtues, or self-control as psychology, or sense of obligation, particularly a moral commitment or sense of duty to honor a contract and re-pay a loan. The pervasive data surveillance of people that goes with “big data” and predictive analytics is not only an invasion of privacy in general, but an impairment of the aspect of privacy called autonomy that will constrict and alter a person’s choices and development of self.
„Before you even know …“ – Big Data und die Erkennbarkeit des Selbst
by Philipp Richter and Andreas Kaminski
Abstract: Der Big Data-Technologie wird das Potenzial zugeschrieben, durch Mustererkennung in aggregierten Daten Verhaltensweisen von Personen zu prognostizieren, noch bevor diese intendiert und reflektiert würden. Zumeist widmet sich die Big Data-Debatte daher den Befürchtungen möglicher Einbußen von Privatheit und Freiheit. In unserem Beitrag wählen wir jedoch einen anderen Zugriff und fragen, inwiefern die Big Data-Visionen das Selbstsein betreffen, also das Konzept davon, wer ich selbst eigentlich bin. Wenn Selbstsein, Martin Heidegger zufolge, eigentlich bedeutet, in kritischer Distanz zu vorgegebenen Möglichkeiten zu leben, stellt sich die Frage, welches Kriterium hier eine klare Unterscheidbarkeit gewährleistet: Wie kann ich wissen, echt ich selbst zu sein, ohne nur wiederum andere Üblichkeiten nachzuahmen? Wir fragen daher, ob die Big-Data-Technologie subjektive Verzerrungen und Täuschungen über das Selbst ausschalten könnte. Hierfür wird der Begriff „Selbstsein“ bei Heidegger erarbeitet. Dabei lässt sich allerdings zeigen, dass dieser letztlich ohne Trennschärfe bleibt, da die nur negative Charakterisierung von eigentlichem Selbstsein, Heidegger zufolge, echt oder unecht ausgestaltet sein kann – der aus der Möglichkeit echter oder unechter Selbsterzählung resultierende Relativismus wurde bisher nicht hinreichend beachtet. Big Data kann zwar uneigentliche Erzählmuster vermeiden, nicht aber uneigentliches oder eigentliches Verhalten differenzieren; ob ich z.B. manches – unecht eigentlich – aus Trotz oder Kalkül gegen mein eigenes Selbstverständnis tat, bleibt verborgen. Fazit: Es muss entweder ein Kriterium für echtes Eigentlichsein entwickelt oder der Anspruch, darüber Auskunft zu geben, verworfen werden – bei Heidegger und Big Data. Verzichtet man jedoch auf die Idee einer (echten) Eigentlichkeit verzichtet man auf den Begriff des Selbst.
The Oracle of Big Data – Prophecies without Prophets
by Bruno Gransche
Abstract: The need for foreknowledge intensifies and a prophetic promise of today’s palm readers causes us wet palms: letting the world speak for itself. Big Data comes with the promise of enabling people to listen to that speaking world and of gaining accurate foreknowledge by Big Data predictions. The uncertainty of our modern, complex world overstrains our present coping capabilities, causing a feeling of slipping off a slippery slope, which in turn causes a need for increasing our own foreknowledge. Part of the Big Data promise is to grant better foreknowledge by overcoming the wrongness of scientific theory or causation assumptions. But thus, people have no other option than to believe in these results and perform their actions in good faith. This makes Big Data based outcomes a matter of faith. This article argues that Big Data based outcomes can be seen as today’s oracle, as prophecies without prophets and reflects on the consequences of that perspective.