PROMOTING SELF-HELP—HOW INTERNET USERS PROTECT THEMSELVES AGAINST ALGORITHMIC RISKS

Abstract

In today’s digitized society, internet users increasingly rely on online services that apply algorithmic selection, like for instance Google Search or the Facebook News Feed. The algorithms that are implemented in these services automatically select information sets and assign relevance to them. This entails societal risks such as privacy breaches, surveillance, manipulation, or overuse. One way for internet users to cope with these risks, is the use of self-help strategies such as deleting cookies or using an adblocker. Therefore, this article wants to answer the following question: What are the factors that promote internet users’ self-help against algorithmic risks? To do so, we analyze nationally representative survey data for three types of algorithmic risks: surveillance, manipulation, and internet overuse. The structural equation models show that being aware of algorithmic risks (H1), having had negative experiences that are related to these risks (H2) and possessing a higher level of internet skills (H3) are positively associated with the use of self-help strategies against algorithmic risks. Therefore, we conclude that awareness of algorithmic risks and internet skills should be promoted to increase internet users’ self-help. Nevertheless, self-help can only complement—but not substitute—statutory regulation to attenuate algorithmic risks.