Lodder, Arno R.; Loui, Ronald P --- "Data algorithms and privacy in surveillance: on stages, numbers and the human factor" [2018] ELECD 1415; in Barfield, Woodrow; Pagallo, Ugo (eds), "Research Handbook on the Law of Artificial Intelligence" (Edward Elgar Publishing, 2018) 375

Book Title: Research Handbook on the Law of Artificial Intelligence

Editor(s): Barfield, Woodrow; Pagallo, Ugo

Publisher: Edward Elgar Publishing

ISBN: 9781786439048

Section: Chapter 13

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

Author(s): Lodder, Arno R.; Loui, Ronald P

Number of pages: 10

Abstract/Description:

Surveillance opportunities are almost limitless with omnipresent cameras, constant internet use, and a wide variety of internet of things objects. We access the Internet anytime, anywhere via smart devices such as phones and tablets, and gradually more objects become connected. In the Internet of Things not only computing devices but all kinds of ordinary objects are provided with an IP address such as books, food, clothes, cars, etc. All our online interactions generate a stream of data. On top of data generated by ordinary internet use, comes the use of wearables, smart meters, connected cars and other features. The process of almost unlimited data generation is labeled as datafication. 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. Data mining and surveillance within the law enforcement and national security contexts raise serious human right rights concerns about the ability of modern States to monitor, oppress and control their citizens. Clearly, data processing algorithms can seriously impact our private lives. 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 aim of this chapter 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: The stages of data processing while using of algorithms, how it affects privacy and what safeguards the law should provide; 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; 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.