Due to well-publicized scandals – from Snowden to Cambridge Analytica – as well as new privacy regulations (chief amongst them GDPR in the EU), online users become increasingly aware of the scale of behavioural tracking practices, and they are clearly worried. According to recent PEW reviews, for instance, only 12% of internet users feel confident that their government to protect their data (Rainie, 2018) and 34% have changed their behaviour because of (suspected) government surveillance (Geiger, 2018).
Research into reactions to online surveillance identified three basic ways online users react to what they perceive as privacy invasions (Poddar et al., 2009):
- Don’t care and continue as they have done before,
- Opt out of services or discontinue the use of certain websites and applications, or
- Strategically manage the information they provide ranging from not providing certain types of information to consciously providing false information or employing privacy-enhancing technologies to mask online behaviours; a strategy we refer to as ‘truth management’
To these three ‘conscious’ strategies we can add a fourth mechanism, namely unconscious changes in user behaviours. Examples are avoiding the use of embarrassing keywords in searches (Marthew and Tucker, 2014) or the reluctance to discuss political topics online and offline (Hampton, Rainie, Lu et al., 2014). Hampton et al.’s findings are especially worrisome as they demonstrate that online monitoring can have chilling effects even in our offline lives.
These conscious and unconscious reactions to online surveillance, create challenges for data analytics, amongst them the increasing likelihood of developing faulty models, a faster decay of models and a general reduction of (cheaply) available data (cp. Bayerl and Akhgar, 2015).
The current reaction is often to develop even more powerful analytics and technological solutions including Artificial Intelligence. On the other side stand efforts invoking ‘counter-movements’ such as “anti-surveillance coats” (projectkovr.com) or “anti-CCTV makeups” (cvdazzle.com) – a dynamic that seems to create ever-faster and sophisticated games of “hide and seek”.
The solution is obviously not to go to the other extreme of ‘no behavioural tracking at all’. Rather, it is a question of managing expectations – and understanding the consequences of the public’s increasing awareness of continuous, deep-reaching surveillance of their daily lives. Sensible data practices remain a balancing act. Some will always stay wary and reluctant to share information online, and it is their good right not to; others are changing their behaviour out of genuine anger or concern.
What needs to come in its place is a better understanding of how surveillance practices are shaping behaviours online and offline and the economic and social costs involved in this continuing cycle of action and reaction. It is time to understand, where behavioural tracking is useful, how it can be done in a sensible way, and where unintended consequences threaten social cohesion as well as the long-term viability of current security practices.
References
Bayerl, P.S., & Akhgar, B. (2015). Surveillance and falsification implications for Open Source Intelligence Investigations. Communications of the ACM, 58(8), 62-69.
Hampton, K.N., Rainie, L., Lu, W., Dwyer, M., Shin, I., & Purcell, K. (2014). Social Media and the ‘Spiral of Silence.’ Pew Research Center, Washington, DC. Available at http://www.pewinternet.org/2014/08/26/social-media-and-the-spiral-of-silence/
Marthew, A. & Tucker, C. (2014). Government Surveillance and Internet Search Behavior, MIT Sloan School of Management. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2412564
Poddar, A., Mosteller, J., & Ellen, P.S. (2009). Consumers’ rules of engagement in online information exchanges. Journal of Consumer Affairs, 43(3), 419-448.