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Privacy, Automation, Legal Automation, Chilling Effects, Human Rights, DMCA, Copyright, Digital Copyright, Digital Millennium Copyright Act, Social Media, Google, Twitter, Social Network, Blogger, Artificial Intelligence, Machine Learning, Micro-Directive, Personalization, Empirical Legal Studies


Advances in artificial intelligence, machine learning, computing capacity, and big data analytics are creating exciting new possibilities for legal automation. At the same time, these changes pose serious risks for civil liberties and other societal interests. Yet, existing scholarship is narrow, leaving uncertainty on a range of issues, including a glaring lack of systematic empirical work as to how legal automation may impact people’s privacy and freedom. This article addresses this gap with an original empirical analysis of the Digital Millennium Copyright Act (DMCA), which today sits at the forefront of algorithmic law due to its automated enforcement of copyright through DMCA notices at mass scale. With literally millions of such notices sent daily, this automation has been criticized for causing large scale chilling effects online, yet few empirical studies have examined this issue in depth. This article does so with a mixed-method empirical legal study synthesizing findings from a survey--with over 1000 participants in a nationally representative sample--with findings from a content analysis of 500 Google Blogs and 500 Twitter accounts targeted by DMCA notices. The study offers a number of new insights, including (1) the DMCA notice and takedown system is likely working for rights-holders with major platforms like Google and Twitter effectively processing the vast majority of DMCA notices they are receiving; (2) DMCA notices may be having broader chilling effects on internet users across a range of activities, with women and the economically disadvantaged likely disproportionately impacted; (3) how the provision of legal information as to internet users' rights can mitigate these chilling effects; and (4) the effectiveness of automated DMCA notices as compared to non-automated ones. The article explores the implications of these findings, including for copyright, algorithmic law, and lays the foundations a privacy theory of automated law and its governance.


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