Five Years of the Right to be Forgotten

By T. Bertram, Elie Bursztein, Stephanie Caro, H. Chao, R. Feman, Peter Fleischer, Albin Gustafsson, Jess Hemerly, Chris Hibbert, L. Invernizzi and 10 more. 2019

44 citations recorded by the source corpus.

The "Right to be Forgotten" is a privacy ruling that enables Europeans to delist certain URLs appearing in search results related to their name. In order to illuminate the effect this ruling has on information access, we conducted a retrospective measurement study of 3.2 million URLs that were requested for delisting from Google Search over five years. Our analysis reveals the countries and anonymized parties generating the largest volume of requests (just 1,000 requesters generated 16% of requests); the news, government, social media, and directory sites most frequently targeted for delisting (17% of removals relate to a requester's legal history including crimes and wrongdoing); and the prevalence of extraterritorial requests. Our results dramatically increase transparency around the Right to be Forgotten and reveal the complexity of weighing personal privacy against public interest when resolving multi-party privacy conflicts that occur across the Internet. The results of our investigation have since been added to Google's transparency report.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science

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Citations in the corpus

Machine Unlearning (2019, methodology): “Indeed, even when conﬁgured in its most accuracy-preserving settings, SISA training can handle orders of magnitude more unlearning requests than what Google expects would be required to implement the right-to-be-forgotten [19].”
DART: Data Addition and Removal Trees (2020, unknown): “Protecting private user data continues to be increasingly important (Shah 2020); for example, Google has implemented a system that removes potentially damaging URLs from their search engine upon request (Bertram et al. 2019).”
Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction (2022, background): “Furthermore, we acknowledge that assessing the overall social impacts of an algorithmic system using only harm as a framing device can miss other negative implications of technology – for instance, inconveniences that arise from exercising the right to data portability or the right to be forgotten [”
When Machine Unlearning Jeopardizes Privacy (2020, background): “2 million requests to delist certain URLs in search results over five years [8].”