Creating Worlds that Create Audiences : Theorising Personal Data Markets in the Age of Communicative Capitalism

In this article, we draw on theories of biopolitical marketing to explore claims that personal data markets are contextualised by what Shoshana Zuboff calls “surveillance capitalism” and Jodi Dean calls “communicative capitalism”. Surveillance and communicative capitalism are characterised by a logic of accumulation based on networked captures of life that enable complex and incomprehensive processes of extraction, commodification, and control. Echoing recent theorisations of data (as) derivatives, Zuboff’s key claim about surveillance capitalism is that data representations open up opportunities for the enhanced market control of life through the algorithmic monitoring, prediction and modification of human behaviour. A Marxist critique, focusing largely on the exploitative nature of corporate data capitalism, has already been articulated. In this article, we focus on the increasingly popular market-libertarian critique that proposes individual control, ownership, and ability to commodify one’s personal data as an answer to corporate data extraction, derivation and exploitation schemes. We critique the claims that personal data markets counterbalance corporate digital capitalism on two grounds. First, these markets do not work economically and therefore are unable to address the exploitative aspect of surveillance capitalism. Second, the notion of personal data markets functions ideologically because it reduces the critique of surveillance capitalism to the exploitation of consumers and conceals the real objective of data capitalists such as Google, Facebook, Amazon and Apple to not (just) exploit audiences but to create worlds that create audiences. Keywords: personal data marketsPeer reviewe

Towards a value theory for personal data

Analysts, investors and entrepreneurs have recognized the value of personal data for Internet economics. Personal data is viewed as the "oil" of the digital economy. Yet, ordinary people are barely aware of this. Marketers collect personal data at minimal cost in exchange for free services. But will this be possible in the long term, especially in the face of privacy concerns? Little is known about how users really value their personal data. In this paper, we build a user-centered value theory for personal data. On the basis of a survey experiment with 1269 Facebook users, we identify core constructs that drive the value of volunteered personal data. We find that privacy concerns are less influential than expected and influence data value mainly when people become aware of data markets. In fact, the consciousness of data being a tradable asset is the single most influential factor driving willingness-to-pay for data. Furthermore, we find that people build a sense of psychological ownership for their data and hence value it more. Finally, our value theory helps to unveil market design mechanisms that will influence how personal data markets thrive: First, we observe a majority of users become reactant if they are consciously deprived of control over their personal data; many drop out of the market. We therefore advice companies to consider user-centered data control tools to have them participate in personal data markets. Second, we find that in order to create scarcity in the market, centralized IT architectures (reducing multiple data copies) may be beneficial

Money Walks: A Human-Centric Study on the Economics of Personal Mobile Data

In the context of a myriad of mobile apps which collect personally identifiable information (PII) and a prospective market place of personal data, we investigate a user-centric monetary valuation of mobile PII. During a 6-week long user study in a living lab deployment with 60 participants, we collected their daily valuations of 4 categories of mobile PII (communication, e.g. phonecalls made/received, applications, e.g. time spent on different apps, location and media, photos taken) at three levels of complexity (individual data points, aggregated statistics and processed, i.e. meaningful interpretations of the data). In order to obtain honest valuations, we employ a reverse second price auction mechanism. Our findings show that the most sensitive and valued category of personal information is location. We report statistically significant associations between actual mobile usage, personal dispositions, and bidding behavior. Finally, we outline key implications for the design of mobile services and future markets of personal data.Comment: 15 pages, 2 figures. To appear in ACM International Joint Conference on Pervasive and Ubiquitous Computing (Ubicomp 2014

End-user Empowerment in the Digital Age

End-user empowerment (or human empowerment) may be seen as an important aspect of a human-centric approach towards the digital economy. Despite the role of end-users has been recognized as a key element in information systems and end-user computing, empowering end-users may be seen as a next evolutionary step. This minitrack aims at advancing the understanding of what end-user empowerment really is, what the main challenges to develop end-user empowering systems are, and how end-user empowerment may be achieved in specific domains

Understanding Engineers' Drivers and Impediments for Ethical System Development: The Case of Privacy and Security Engineering

Machine ethics is a key challenge in times when digital systems play an increasing role in people's life. At the core of machine ethics is the handling of personal data and the security of machine operations. Yet, privacy and security engineering are a challenge in today's business world where personal data markets, corporate deadlines and a lag of perfectionism frame the context in which engineers need to work. Besides these organizational and market challenges, each engineer has his or her specific view on the importance of these values that can foster or inhibit taking them into consideration. We present the results of an empirical study of 124 engineers based on the Theory of Planned Behavior and Jonas' Principle of Responsibility to understand the drivers and impediments of ethical system development as far as privacy and security engineering are concerned. We find that many engineers find the two values important, but do not enjoy working on them. We also find that many struggle with the organizational environment. They face a lack of time and autonomy that is necessary for building ethical systems, even at this basic level. Organizations' privacy and security norms are often too weak or even oppose value-based design, putting engineers in conflict with their organizations. Our data indicate that it is largely engineers' individually perceived responsibility as well as a few character traits that make a positive difference

Can you own your personal data? The HAT (Hub-Of-All-Things) data ownership model

This paper sets out 11 principles of the HAT data ownership model as the basis for the legal, economic and technical engineering of personal data rights for individuals, sui generis, through the HAT Microserver artefact and re-commodification of personal data into a new asset class of PPD (person-controlled personal data) for a market to emerge. We argue that the formation of PPD as an asset class can emerge a primary market for personal data due to its ability to create differential privacy through selected data (without revealing personal identifying information), bundled multi-source data from the individuals themselves that is verifiable, data that is shareable in real time and on demand from the cloud and that is dynamically accurate, due to individuals themselves being the stakeholders of their data. The HAT Project’s ultimate objective is that an explicit, primary market for personal data, similar to the emergence of a primary market for digital music in the early 2000s, would reduce illegal and inefficient personal data markets as well as reduce externalities relating to privacy, as future applications switch to using HATs as user accounts. The HAT model sets up a parallel asset class to challenge the OPD asset class through easier access, higher quality and lower friction, much like the way music licensees challenged music piracy

[How] Can Pluralist Approaches to Computational Cognitive Modeling of Human Needs and Values Save our Democracies?

In our increasingly digital societies, many companies have business models that perceive users’ (or customers’) personal data as a siloed resource, owned and controlled by the data controller rather than the data subjects. Collecting and processing such a massive amount of personal data could have many negative technical, social and economic consequences, including invading people’s privacy and autonomy. As a result, regulations such as the European General Data Protection Regulation (GDPR) have tried to take steps towards a better implementation of the right to digital privacy. This paper proposes that such legal acts should be accompanied by the development of complementary technical solutions such as Cognitive Personal Assistant Systems to support people to effectively manage their personal data processing on the Internet. Considering the importance and sensitivity of personal data processing, such assistant systems should not only consider their owner’s needs and values, but also be transparent, accountable and controllable. Pluralist approaches in computational cognitive modelling of human needs and values which are not bound to traditional paradigmatic borders such as cognitivism, connectionism, or enactivism, we argue, can create a balance between practicality and usefulness, on the one hand, and transparency, accountability, and controllability, on the other, while supporting and empowering humans in the digital world. Considering the threat to digital privacy as significant to contemporary democracies, the future implementation of such pluralist models could contribute to power-balance, fairness and inclusion in our societies

Big Data Privacy Context: Literature Effects On Secure Informational Assets

This article's objective is the identification of research opportunities in the current big data privacy domain, evaluating literature effects on secure informational assets. Until now, no study has analyzed such relation. Its results can foster science, technologies and businesses. To achieve these objectives, a big data privacy Systematic Literature Review (SLR) is performed on the main scientific peer reviewed journals in Scopus database. Bibliometrics and text mining analysis complement the SLR. This study provides support to big data privacy researchers on: most and least researched themes, research novelty, most cited works and authors, themes evolution through time and many others. In addition, TOPSIS and VIKOR ranks were developed to evaluate literature effects versus informational assets indicators. Secure Internet Servers (SIS) was chosen as decision criteria. Results show that big data privacy literature is strongly focused on computational aspects. However, individuals, societies, organizations and governments face a technological change that has just started to be investigated, with growing concerns on law and regulation aspects. TOPSIS and VIKOR Ranks differed in several positions and the only consistent country between literature and SIS adoption is the United States. Countries in the lowest ranking positions represent future research opportunities.Comment: 21 pages, 9 figure