Measuring surveillance chill and other regulatory impacts at scale
Jonathon W. Penney · Edward Elgar Publishing eBooks · 2020
With digital surveillance and censorship on the rise, the amount of data available online unprecedented, and governments and businesses increasingly employing emerging technologies like artificial intelligence (AI), machine learning, and facial recognition technology (FRT) to automate surveillance, legal enforcement, and to protect and promote private and corporate legal interests, concerns about the large-scale impact of these state and corporate regulatory activities—particularly their “chilling effects”, that is, the capacity to “chill” or deter people from exercising their rights and freedoms—have taken on greater urgency and importance, especially with the present dearth of systematic study on point. This chapter argues that the emerging field of computational legal studies can play a unique role in helping address this research gap—to substantiate, and provide essential insights into, impacts of surveillance and other emerging forms of regulatory activities—like automated legal enforcement. The chapter also illustrates this argument via a computational legal studies (CLS) case study that explores the impact of online surveillance on Wikipedia users and makes recommendations for CLS research in this area moving forward.