Privacy and Legal Automation: The DMCA as a Case Study Jonathon W. Penney* 22 STAN. TECH. L. REV. 412 (2019) ABSTRACT 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 study synthesizing survey-based findings with an analysis of 500 Google Blogs and 500 Twitter accounts that have received DMCA notices. The findings offer a number of new insights, including evidence for DMCA notice * The author would like to thank Victoria Nash, Urs Gasser, Joss Wright, Ian Brown, David Erdos, Jennifer Urban, Ari Waldman, Eric Goldman, Joe Kariganis, Annemarie Bridy, Niva Elkin-Koren, J. Nathan Matias, Ryan Budish, Kendra Albert, Andy Sellars, Molly Sauter, Derek Bambauer, Ben Zevenbergen, Neil Richards, Daphne Keller, and Jennifer Daskell for comments, advice, and/or suggestions or previous drafts or presentations based thereon. The author would also like to thank participants for his talks at the 2016 Internet Law Work in Progress Conference, Santa Clara Law School, Santa Clara University, March 2016; Berkman Klein Center for Internet & Society, Harvard University, May 2016; Luncheon Speaker Series, Center for Information Technology Policy, Princeton University, March 27, 2018; and Intellectual Property Law Discussion Group, Faculty of Law, University of Oxford, May 8, 2018. Spring 2019 PRIVACY AND LEGAL AUTOMATION 413 chilling effects across a range of activities; support for a privacy theory of automated law chilling effects; evidence of differential impacts including that women are disproportionately chilled and that legal information can mitigate chilling effects; and the effectiveness of automated DMCA notices as compared to non-automated ones. This article also explores the implications of these findings for future forms of automated law and lays the foundations for a new theory of governance for personal legal automation. TABLE OF CONTENTS I. INTRODUCTION ........................................................................................................... 414 II. THEORIZING LEGAL AUTOMATION AND THE DMCA .............................................. 422 A. The Automated Notice Scheme .............................................................................. 422 B. Personal and Impersonal Automated Legal Enforcement ...................... 426 C. DMCA Automation and Chilling Effects ............................................................. 430 III. METHOD AND DESIGN ................................................................................................ 436 A. Case Study One: DMCA Online Survey ................................................................ 436 B. Case Study Two: Google Blogs and Twitter Analysis .................................. 439 C. Hypotheses / Predictions .......................................................................................... 443 IV. RESULTS AND DISCUSSION......................................................................................... 445 A. DMCA Impact from Survey Analysis .................................................................... 445 1. DMCA Impact on Activities Online .............................................................. 445 2. Impact of a “Friend” Receiving DMCA Notice ....................................... 447 3. Predicting Impacts .............................................................................................. 448 4. Legally Challenging DMCA Notices............................................................. 451 B. Impact from Blog and Twitter Analysis ............................................................ 453 1. Information on Sample of DMCA Notices ................................................ 453 2. Impact of the DMCA notices ........................................................................... 455 3. Impact of Automated vs. Non-Automated Notices ............................. 461 V. IMPLICATIONS ............................................................................................................. 463 A. Chilling Effects and Other Impacts ...................................................................... 463 1. Evidence of Chilling Effects ............................................................................. 464 2. “Networked”/Indirect Chilling Effects ...................................................... 467 B. A Privacy Theory of Automated Law Chilling Effects ................................ 468 C. Differential Impact ....................................................................................................... 470 1. Women Disproportionately Impacted ...................................................... 470 2. Knowledge Is Power: Mitigating Chill with Legal Information ... 471 D. The Efficiency of Automated vs. Non-Automated Notices ....................... 471 E. Micro-Directives and the Future of Legal Automation ............................. 472 F. Toward a Theory of Personal Legal Automation ......................................... 475 VI. CONCLUSION................................................................................................................ 480 APPENDIX A. BLOGGER SAMPLE: CODING ONLINE/OFFLINE CONTENT STATUS ...... 483 APPENDIX B. TWITTER SAMPLE: CODING ONLINE/OFFLINE CONTENT STATUS ...... 484 414 STANFORD TECHNOLOGY LAW REVIEW Vol. 22:2 I. INTRODUCTION Automated legal enforcement is here,1 but its future is uncertain and its actual benefits, limits, and impact is unclear.2 A combination of rapidly developing computing technology along with advances in artificial intelligence (“AI”), machine learning, and availability of big data are creating exciting new possibilities for the automation of law and legal enforcement, with the potential of significant benefits like greater efficiency, scalability, and costs savings.3 Yet these same possibilities and new legal automation applications—like predictive policing, AI-powered surveillance, or personalized algorithmic legal enforcement at mass scale—raise a whole host of complex law and policy issues, including human rights, transparency, equality, and due process.4 Despite these 1. Woodrow Hartzog et al., Inefficiently Automated Law Enforcement, 2015 MICH. ST. L. REV. 1763, 1764 (2015) (“While it may sound like science fiction, the automation of law enforcement is already here.”); Frank Pasquale & Glyn Cashwell, Four Futures of Legal Automation, 63 UCLA L. REV. DISCOURSE 26, 36, 39 (2015) (“The automation of law enforcement is already well documented in many fields.”); Lisa A. Shay et al., Confronting Automated Law Enforcement, in ROBOT LAW 235, 235 (Ryan Calo et al. eds., 2016); Thomas A. Smith, From Law to Automation, 1 CRITERION J. ON INNOVATION 535, 536 (2016); Manuel A. Utset, Digital Surveillance and Preventive Policing, 49 CONN. L. REV. 1453 (2017); Benjamin Alarie et al., Regulation by Machine, PROC. 29TH CONF. ON NEURAL INFO. PROCESSING SYS. (NIPS), Dec. 5-10, 2016, https://perma.cc/Z6AF-ZVLX . On the broader issues raised by automation of legal processes, see also Thomas H. Davenport & Jeanne G. Harris, Automated Decision Making Comes of Age, 46 MIT SLOAN MGMT. REV., Jul. 15, 2005, at 84, https://perma.cc/9ZUF-689A. 2. Pasquale & Cashwell, supra note 1, at 28 (“The future of law and computation is more open ended than most commentators suggest.”); Woodrow Hartzog, On Questioning Automation, 48 CUMB. L. REV. 1, 1–2 (2017) (describing the uncertainty of costs and benefits of technologies leveraging AI and algorithms); Anthony Niblett, Regulatory Reform in Ontario: Machine Learning and Regulation C.D. HOWE INST. COMMENT. No. 507, Mar. 2018, at 3–4; Frank Pasquale, A Rule of Persons, Not Machines: The Limits of Legal Automation, 87 GEO. WASH. L. REV. 1 (2019) (on the limits of legal automation); Frank Pasquale & Glyn Cashwell, Prediction, Persuasion, and the Jurisprudence of Behaviourism, 68 U. TORONTO L.J. 63, 63 (2018) (questioning predictions about the future of legal automation); See also FRANK PASQUALE, THE BLACK BOX SOCIETY (2015). 3. Hartzog et al., supra note 1, at 1765 (“The benefits that robotic technology will bring to law enforcement—particularly in the areas of efficiency and cost savings—are theoretically impressive.”); Hartzog, supra note 2, at 1 (“Given the rapid pace of innovation and adoption, it can be hard to make sense of automated technologies”). 4. See, e.g., Hartzog et al., supra note 1, at 1765 (“[E]mployment of these technologies without careful consideration poses a distinct danger to our civil liberties and can have detrimental effects on society.”); Pasquale & Cashwell, supra note 1, at Spring 2019 PRIVACY AND LEGAL AUTOMATION 415 challenges and real uncertainties about