This leads to the conclusion that the conduct of companies working with big data might lack ethical justification. World Economic Forum ; Chen et al. Advanced data mining techniques allow companies Footnote 2 to generate non-trivial new insights out of existing data. Since collecting more data always translates itself into more potential new insights waiting to be extracted from the data, data hungriness is a structural condition of the big data world we have come to inhabit. This observation already goes a long way in explaining why many privacy worries are raised in the context of big data.

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This leads to the conclusion that the conduct of companies working with big data might lack ethical justification. World Economic Forum ; Chen et al. Advanced data mining techniques allow companies Footnote 2 to generate non-trivial new insights out of existing data.

Since collecting more data always translates itself into more potential new insights waiting to be extracted from the data, data hungriness is a structural condition of the big data world we have come to inhabit. This observation already goes a long way in explaining why many privacy worries are raised in the context of big data.

In this article, I take these privacy worries as a point of departure, but I focus on the conduct of big data companies and, more specifically, on some of the fundamental assumptions underlying their conduct and that have remained implicit thus far. Looking at the conduct of typical big data companies such as Acxiom, Bloomreach, Lotame, Palantir, Google, APT, Facebook, and Marketshare, we see that their success is largely dependent on their ability to generate new, non-trivial insights out of existing data.

Footnote 4 Besides this ability to create new insights and one may even say new data out of existing data, the business model of companies such as the ones mentioned is also premised on the fact that creators of these new insights may appropriate these new insights. The question I want to take up here is whether this ethical judgment is a legitimate one. As a result, it is far from obvious that the business of big data companies is legitimate from an ethical point of view.

Against the background of potential threats of big data that other scholars have formulated e. Ultimately, my argument may have legal and political consequences concerning the regulation of big data companies. This article proceeds in four steps. Firstly, the concept of big data will be introduced. Fourth and lastly, conclusions will be drawn on the basis of the preceding arguments. Big data Big data is a notoriously messy concept, so let me first explain what I take the concept big data to mean before proceeding.

My description here is tentative and does not do justice to the great variety of academic literature dealing with big data.

I choose to focus only on those aspects of big data that I need for my argument since my space is limited. It is clear that the ability to collect and store larger volumes of data than ever before is a driving force behind the phenomenon of big data.

This does not mean, however, that what makes big data big is simply a certain, large enough, volume of data. The transition from data to big data is not just a quantitative shift, it is a qualitative shift as well.

Big data is not so much about amounts of data, as it is about thinking about data, dealing with data, and approaching challenges and opportunities through the eyes of data. In big data contexts, sheer size and volume are supposed to make up for messiness and low quality data.

What is of great importance to them is the idea that in a big data world more data from various sources, even sources of dubious quality, combined in a larger dataset will almost always lead to more powerful and valuable analyses. When using a big data approach to a problem, the goal is not to amass as much data as possible in order to simply paint an as accurate as possible picture.

As an effect, storing information—even if one is not sure how useful the data are right now—becomes more and more interesting. It incentivizes collection of more data for longer periods of time. Data mining is the technique that can be seen as the big data analysis technique par excellence. In order to achieve this extraction or generating Footnote 10 of new, emergent data, a combination of complex algorithms and brute computing force are used to work on the data.

The fact that we can discover new knowledge in existing data by using data mining techniques goes a long way in explaining why big data is a phenomenon that attracts so much attention. It surrounds big data with an aura of entrepreneurship. Entrepreneurs who work with big data hope that they will be the first to awaken the dormant value that lies hidden in big data datasets.

The often used metaphors of data as the new oil and of datasets as goldmines with nuggets of gold hidden inside those datasets are expressions of this entrepreneurial potential.

In this section I want to shift the focus to the domain of ethics. This question of legitimate appropriation is ultimately and ethical judgment that relies on substantial normative assumptions that can and should be scrutinized.

Kirzner has formulated it. This will help us to better understand the presupposed ethic of big data business. In the next section, I will problematize the normative assumptions of finders—keepers in big data contexts. The economic insight is that which permits us to perceive the discovery of a hitherto unknown market use for an already owned resource or commodity as the discovery of and consequently the spontaneous establishment of ownership in a hitherto un-owned element associated with that resource or commodity Kirzner : This means precisely what it appears to mean: those who find something that is not held by anybody, are, as they found it, the legitimate owners of that which they have discovered.

Kirzner, however, proposes to reconceptualize what discovering something that was previously unheld means. This is a significant reconceptualization since creation is a substantially different act than acquisition from nature. The justness of the transfer could then be subject to ethical scrutiny.

If the finder has created the goods by finding them, they cannot transfer from nature to the finder for the simple fact that the goods did not exist, in the relevant sense, in nature before they were found. Footnote 12 Entrepreneurs, however, do not—or not exclusively—appropriate unheld resources, but also acquire held resources via just transfers, apply an entrepreneurial insight to create more commercial value, and then profit from these improvements.

Those are two different situations, although the way they have to be understood according to Kirzner will turn out to be remarkably similar. Put differently, the owner of a resource can only be the owner of those properties and potential applications of a resource that the owner is explicitly aware of.

This also means that the newly created value was not transferred from the original holder of the oranges to the entrepreneur, since this created value came into existence after the entrepreneur acquired the oranges and applied her insights to the product. That part of the transaction which allows the entrepreneur to be an entrepreneur was, properly speaking, never part of the transaction. The concept transaction implies that the element of the good that is exploited by the entrepreneur to allow for her profitable insight was first held by the original holder and later, after the transaction, by the entrepreneur.

But this is not the case, because the entrepreneur created the additional value ex nihilo, meaning that the initial seller never possessed it to begin with. Big data and finders—keepers My claim now is that this notion of finders—keepers appears to be presupposed by those companies working with big data.

These companies use, just like the orange juice entrepreneur, specific resources to create something new out of these resources. In the case of big data, personal data are used to extract non-trivial new information out of the given data via the technique of predictive data mining.

The big data entrepreneurs then appropriate the fruits of the newly discovered insights. And just like in the case of the oranges, we can ask whether the big data entrepreneur can legitimately appropriate the fruits of these new insights. As long as the big data entrepreneur gets a hold of the original personal data in a just way, the entrepreneur is free to apply entrepreneurial insights and appropriate the additional value that she creates.

Just like the original holder of the oranges was never the owner of the property of the oranges that allowed the entrepreneur to make orange juice out of the oranges, so the data subjects, whose data are used, were never the owners of those valuable insights that lie hidden in the data and that the big data entrepreneurs manage to extract.

The data subjects providing the data cannot, in providing the data, be explicitly aware of the specific valuable insights that are hidden in their data. To see why, remember that these insights are in fact new non-trivial data, created out of the original data.

All three assumptions are problematic due to their insensitivity to the specificity of what kind of things personal data are and the functioning of personal data in big data contexts. As the discussion of these problematic assumptions shows, explicating the normative basis of big data entrepreneurship allows for new types of critique on the conduct of big data companies.

This introduces a certain kind of divisibility to goods which is necessary for finders-keepers to function adequately. In the case of inanimate objects this theory may be plausible—although even in those cases the divisibility of objects might feel highly artificial.

But even if we assume, for the sake of argument, that this divisibility is plausible and accepted by everyone in the case of inanimate objects, it still does not follow that it is, by extension, equally plausible to think of personal data in a similar fashion. Granted, we often do speak of personal data as something—a resource, a thing—that can be owned, but does that automatically mean that personal data are to be understood as nothing more than inanimate objects?

I believe that the relationship between a person and her data is not exactly the same as the relationship between a person and a quotidian object a phone, an orange, etc. If we understand the relation between an individual and her personal data the way Floridi does, it becomes immediately clear that it is far from unproblematic to conceive of personal data as if they were like oranges and orange juice.

Footnote 14 As an effect, additional arguments are needed to extend this idea of divisibility from inanimate objects to personal data. At this point, the objection might be raised that big data analyses do not even need personal data to be effective.

Completely anonymized data can also do the trick in some instances. This definition hinges on the question whether a piece of information or data can be explicitly related back to a person. If this standard definition is adopted, my argument my indeed seem dubious. Even data that cannot be directly related to natural persons can be used, in big data contexts, to generate insights that can nonetheless have a significant impact on the lives and self-understanding of persons.

Think for instance of discriminatory targeting practices as described by Turow that need not necessarily be based on personal data in the legal sense of the word to still have those discriminatory effects. I want to propose that in those cases where, legally speaking, anonymized and therefore non-personal data are used, there is still something personal about the data in a moral sense. Because these data can still have a significant influence on the lives and self-understandings of persons and are, seen from this perspective, still constitutive of personhood, I believe it makes sense to say that these data are still, in a moral sense, personal.

As a result, it is still unconvincing to assume, without argument, that these data can be treated as just any quotidian object. Acquisition of personal data The acquisition of personal data—understood in the broader sense advocated above—by big data companies has not been problematized thus far. It has simply been assumed that big data companies acquire personal data in a just manner on the market, by way of transactions based on mutual consent.

The idea that personal data are usually acquired in a just manner by big data companies because individuals consent to it may seem plausible. In reality, however, this position is quite hard to maintain. The idea that these transactions of personal data are based on informed consent, and that this informed consent is truly informed consent, is not very convincing in the face of the apparent failures of the informed consent model.

Zuiderveen Borgesius investigates the actual functioning of the informed consent model for the placement of cookies on computers and concludes that informed consent mechanisms are not strong enough to protect individuals.

But for informed consent to function properly, this model of man as a perfect homo economicus must be somewhat adequate, and it is far from obvious that it is.

The problems of the informed consent model can potentially erode the legitimacy of the original acquisition of the personal data that are used by big data companies. This, in turn, raises the question whether the appropriation of newly mined insights can be just if the data entrepreneurs work with to generate these insights were not acquired justly.

Historical conception of justice Finders—keepers presupposes a historical conception of justice Kirzner : 9. A clear formulation of this historical conception can be found in Nozick : — A historical conception of justice evaluates outcomes by focusing exclusively on two questions: 1 was the original acquisition just, and 2 were all the subsequent transfers just.

If both conditions are satisfied, then outcomes must necessarily be just. As an effect, outcomes cannot be evaluated in their own right. This conception of justice is problematic in big data contexts since an exclusive focus on the original acquisition of data and the subsequent transfers of data does not allow us to deal adequately with the challenges big data presents us with.

As was already shown, one of the unique aspects of big data is that outcomes are inherently unpredictable.

Therefore, an exclusive focus on individual transactions, without focus on the actual aggregated outcomes these transactions can lead to, will necessarily miss something important.

Not being able to evaluate these unpredictable outcomes in their own right is a serious problem for any analysis of big data that wants to focus on the desirability of certain applications and their outcomes. This ethic serves the purpose of legitimizing the appropriation of newly mined, potentially profitable insights by these big data companies.

However, because this hidden normative manipulative basis of big data entrepreneurship has remained implicit thus far, no explicit arguments have been provided in favor of it.

This is problematic, for the plausibility of finders—keepers in the context of big data is far from self-evident.


Losers weepers? Readers share stories of lost property honesty … and greed

By Lucy Rodgers BBC News Who should legally keep the winnings from a lost lottery ticket - the woman who mislaid it, or the married couple who found it? It is an age-old dilemma. And how about if you discover something worth hundreds or thousands of pounds, such as jewellery or a winning lottery ticket? While some may hand lost property in to the authorities, many others seek justification in the playground chant "finders keepers, losers weepers". Yet, although the adage is often quoted by those who claim rights over their discoveries, the recent case of Wiltshire couple Amanda and Michael Stacey shows it holds little sway in a court of law.


Finders keepers, losers weepers?


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