Data algorithms and privacy in surveillance: on stages, numbers and the human factor

Humans can process only small amounts of data, computers can process almost infinitely. But also computers need to act smart or intelligent, because otherwise even they get swamped and/or might produce useless information. Algorithms help in structuring and analyzing vast amounts of data. With the growth of data we have to rely increasingly on algorithms. These algorithms may perform better than alternative approaches we used to rely on. However, algorithms can be opaque, and the danger is that we get obscured by algorithms. Intelligence agencies use algorithms to distinguish between persons of interest and others. Law enforcement uses analytics and data mining to identify suspects and to support investigations. Businesses profile users in all kind of categories. Surveillance is omnipresent. The impact on privacy is not necessarily depending on who does the surveillance. It depends not only on the actors, but on various factors. Sometimes what businesses do impacts severely on the privacy of consumers, sometimes the work of police or intelligence agencies does not. In this paper we focus on surveillance by the US National Security Agency (NSA) and other intelligence agencies. Our aim is to dissect data analytics by intelligence agencies, and to suggest what privacy related law should focus on more than it does today. With an understanding of how big data algorithms usually work we discuss in this chapter the use of algorithms from a privacy and data protection angle. First, we briefly introduce the central concepts of data protection and privacy against the background of the General Data Protection Regulation introduced by the European Union in 2012, published in 2016 and effective as of 25 May 2018. The core of the chapter consists of elaborating upon three issues: 1. The stages of data processing while using of algorithms, how it affects privacy and what safeguards the law should provide; 2. The role of the human factor: how and when should humans be involved in evaluating outcomes, and also under what circumstances human interference is better abstained from; 3. The relevance of scale and scope: in the light of privacy, numbers matter. However, so far in law a discussion on the relevance of numbers (or scale) is largely absent.