Daniel Seng, ‘Copyrighting Copywrongs: An Empirical Analysis of Errors with Automated DMCA Takedown Notices’

Under the Digital Millennium Copyright Act (DMCA), reporters issuing takedown notices are required to identify the infringed work and the infringing material and provide their contact information (functional formalities), attest to the accuracy of such information and their authority to act on behalf of the copyright owner, and sign the notices (non-functional formalities). Online service providers will evaluate such notices for compliance with these DMCA formalities before acting on them. This paper seeks to answer questions about the quality of takedown notices, especially those generated by automated systems, which are increasingly being used by copyright owners to detect instances of online infringement and issue takedown notices on their behalf.

After parsing three million takedown notices and more than eighty million takedown complaints served on Google between 2011 and 2015, this paper analyzes each notice for errors. This paper finds that almost all notices comply with the non-functional formalities. However, at least 5.5% of all takedown notices between 2011 and 2015 fail to comply with the functional formalities in that they are missing copyright work descriptions. In addition, at least 9.8% of the takedown notices exhibit have empty takedown requests, misidentify the infringing site or provide inactive URIs as takedown requests. To ensure that the takedown system remains fast, efficient and error-free, this paper proposes to strengthen the attestation requirements of notices, to require reporters to validate all submitted takedown complaints and requests, and to subject recalcitrant reporters to the ‘slow lane’ of a two-tier system for processing takedown notices. This methodology reflects the use of accountability metrics in the design of automated systems and suggests a verifiable response to address concerns pertaining to the use of systems that supplant human decision making

Seng, Daniel, Copyrighting Copywrongs: An Empirical Analysis of Errors with Automated DMCA Takedown Notices, 37 Santa Clara High Technology Law Journal 119 (2021).

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