Scale and Scope

Surveillance capital leads to intense competition for behavioural surplus and prediction products and it is no longer enough to have a high volume of surplus (scale). A variety of surplus (scope) is also needed. The user’s mobile phone becomes the critical tool with economies of scope working in two dimensions12. Firstly, to extend out as far as possible by capturing locations and actions and then extending as deeply as possible by capturing feelings and emotions through the analysis of user images, videos and voice3. But the most predictive surplus comes from intervening in users activities, and herding users in specific directions4. This competition has resulted in pressures being applied to new and established businesses to leverage their data to create products for digital prediction markets5.

Dark Patterns

In the age of surveillance capitalism, the primary goal is to maximize user engagement while minimizing the awareness of dataveillance activities happening in parallel. The term “dark patterns” is used in the app design community to describe design patterns that are not in the user’s best interests or not optimized for the user6. In the case of surveillance capitalism, an app’s user interface is optimized for data capture rather than for optimal user experience. Faced with the competitive pressures of surveillance capitalism, app designers may have to prioritize data capture over user experience in order to remain competitive in the market place.

Privacy by Design - PbD

Some scholars have proposed steps to safeguard digital privacy. Cavoukian’s (2012)7 Privacy by Design (PbP) framework can be used when considering digital privacy and includes principles such as privacy by default, privacy embedded in core architecture, secure communications and transparency and respect for user privacy. PbD principles encourage the use of methods such as encryption during transmission which would significantly enhance security, even when using platforms controlled by surveillance capitalist firms. The metadata would still be exposed to data harvesting, but the user data would enjoy significant protection.

Data prediction markets provide motivation for malware and phishing attacks8. Malware and phishing attacks are used to harvest data for sale on the data markets91011 and in these cases, following PbD guidelines by increasing the security robustness of apps and awareness of attack vectors could enhance users privacy.

Next: Instrumentarian Power

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  2. Zuboff, S., 2019. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. Profile Books. ↩︎

  3. Cambria, E., 2016. Affective Computing and Sentiment Analysis. IEEE Intell. Syst. 31, 102–107. ↩︎

  4. Wykes, T., 2019. Racing towards a digital paradise or a digital hell? J Ment Health 28, 1–3. ↩︎

  5. Shimp, G., 2017. How Digital Transformation Is Rewriting Business Models [WWW Document]. URL (accessed 4.29.19). ↩︎

  6. Zagal, J.P., Björk, S., Lewis, C., 2013. Dark Patterns in the Design of Games. Presented at the Foundations of Digital Games 2013. ↩︎

  7. Cavoukian, A., 2012. Privacy by Design [Leading Edge]. IEEE Technol. Soc. Mag. 31, 18–19. ↩︎

  8. Felt, A.P., Finifter, M., Chin, E., Hanna, S., Wagner, D., 2011. A Survey of Mobile Malware in the Wild, in: Proceedings of the 1st ACM Workshop on Security and Privacy in Smartphones and Mobile Devices, SPSM ’11. ACM, New York, NY, USA, pp. 3–14. ↩︎

  9. Bhandari, S., Jaballah, W.B., Jain, V., Laxmi, V., Zemmari, A., Gaur, M.S., Mosbah, M., Conti, M., 2017. Android inter-app communication threats and detection techniques. Comput. Secur. 70, 392–421. ↩︎

  10. Jain, A.K., Shanbhag, D., 2012. Addressing Security and Privacy Risks in Mobile Applications. IT Prof. 14, 28–33. ↩︎

  11. Wright, R.T., Jensen, M.L., Thatcher, J.B., Dinger, M., Marett, K., 2014. Research Note—Influence Techniques in Phishing Attacks: An Examination of Vulnerability and Resistance. Inf. Syst. Res. 25, 385–400. ↩︎