Citekey: @Zwitter2014

Zwitter, A. (2014). Big Data ethics. Big Data & Society, 1(2). doi:10.1177/2053951714559253


A primer of Big Data ethics. Considering adding it to the reading list of the Tech & Ethics course.


Hence, much less commonly discussed are the ethical implications of impersonal data. Examples include, among others, the ‘‘likes’’ on Facebook sold to marketing companies in order to more specifically target certain micro-markets; information generated out of Twitter feed based sentiment analyses for political manipulation of groups, etc. (p. 1)

ethicists have to reconsider some traditional ethical conceptions. (p. 1)

First, it will briefly outline the traditional ethical principles with regard to moral responsibility. Thereafter, it will summarize four qualities of Big Data with ethical relevance. The third delves deeper into the idea of the changing nature of power and the emergence of hypernetworked ethics; and the fourth section illustrates which ethical problems might emerge in society, politics and research due to these changes. (p. 1)

The novelty of Big Data poses ethical difficulties (such as for privacy), which are not per se new. (p. 1)

In addition to its novelty, the very nature of Big Data has an underestimated impact on the individual’s ability to understand its potential (p. 1)

Traditional ethics (p. 2)

At the heart of Big Data are four ethically relevant qualities: (p. 2)

Since the enlightenment, traditional deontological and utilitarian ethics place a strong emphasis on moral responsibility of the individual, often also called moral agency (MacIntyre, 1998). This idea of moral agency very much stems from almost religiously followed assumptions about individualism and free will. (p. 2)

  1. There is more data than ever in the history of data (Smolan and Erwitt 2012): (p. 2)

  2. Big Data is organic: although this comes with messiness, by collecting everything that is digitally available, Big Data represents reality digitally much more naturally than statistical data—in this sense it is much more organic. (p. 2)

In general, the moral agency is determined by several entity innate conditions, three of which are commonly agreed upon (Noorman, 2012): (p. 2)

  1. Causality: An agent can be held responsible if the ethically relevant result is an outcome of its actions. 2. Knowledge: An agent can be blamed for the result of its actions if it had (or should have had) knowledge of the consequences of its actions. 3. Choice: An agent can be blamed for the result if it had the liberty to choose an alternative without greater harm for itself. (p. 2)

  2. Big Data is potentially global: not only is the representation of reality organic, with truly huge Big Data sets (like Google’s) the reach becomes global. (p. 2)

  3. Correlations versus causation: Big data analyses emphasize correlations over causation. (p. 2)

New advances in ethics have been made in network ethics (Floridi, 2009), the ethics of social networking (Vallor, 2012), distributed and corporate moral responsibility (Erskine, 2004), as well as computer and information ethics (Bynum, 2011). (p. 2)

distributed morality (Floridi, 2013; Noorman, 2012), which need to be raised (p. 2)

New power distributions (p. 2)

Four qualities of Big Data (p. 2)

Many books on computer ethics and cyber ethics have been written in the past three decades since, among others, Johnson (1985) and Moor (1985) established the field. (p. 2)

the ethics of Big Data moves away from a personal moral agency in some instances (p. 2)

In general, however, the trend is towards an impersonal ethics based on consequences for others. (p. 2)

The interaction between these three stakeholders illustrates power relationships and gives us already an entirely different view on individual agency, namely an agency that is, for its capability of morally relevant action, entirely dependent on other actors. One could call this agency ‘dependent agency’ (p. 3)

Moreover, in a hyperconnected era, the concept of power, which is so crucial for ethics and moral responsibility, is changing into a more networked fashion. (p. 3)

There are three categories of Big Data stakeholders: Big Data collectors, Big Data utilizers, and Big Data generators. (p. 3)

The more connections A has, the more power he or she can exert. This is referred to as micro-level power and is understood as the concept of centrality (Bonacich, 1987). (p. 3)

In terms of Big Data stakeholders, this could mean that we find these new stakeholders wielding a lot of power: a. Big Data collectors determine which data is collected, which is stored and for how long. They govern the collection, and implicitly the utility, of Big Data. b. Big Data utilizers: They are on the utility production side. While (a) might collect data with or without a certain purpose, (b) (re-)defines the purpose for which data is used, for example regarding: (p. 3)

Some ethical Big Data challenges (p. 3)

Big Data is the effect of individual actions, sensory data, and other real world measurements creating a digital image of our reality. Cukier (2013) calls this ‘‘datafication’’. (p. 3)

Privacy (p. 3)

Group privacy (p. 4)

Propensity (p. 4)

The movie Minority Report painted a vision of a future in which predictions about what people were likely to do could lead to their incarceration without an act committed. (p. 4)

To strip data from all elements pertaining to any sort of group belongingness would mean to strip it from its content. (p. 4)

‘‘predictive policing’’ (p. 4)

Big Data might induce certain changes to traditional assumptions of ethics regarding individuality, free will, and power. This might have consequences in many areas that we have taken for granted for so long. (p. 5)

In other words, Big Data makes the likelihood of random findings bigger—something that should be critically observed with regard to investigative techniques such as RIOT. (p. 5)

Research ethics (p. 5)

Ethical codes and standards with regard to research ethics lag behind this development. (p. 5)

Eventually, ethicists will have to continue to discuss how we can and how we want to live in a datafied world and how we can prevent the abuse of Big Data as a new found source of information and power. (p. 5)

Research findings that reveal uncomfortable information about groups (p. 5)

Another problem is the ‘‘informed consent’’: despite the data being already public, no one really considers suddenly being the subject of research in Twitter or Facebook studies. (p. 5)

Conclusions (p. 5)

Bynum T (2011) Computer and information ethics. In: Zalta EN (ed) The Stanford Encyclopedia of Philosophy. Available at: entries/ethics-computer/ (accessed 23 July 2014). (p. 6)

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Bodong Chen, University of Minnesota

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