Personalization on digital platforms drives a broad range of harms, including misinformation, manipulation, social polarization, subversion of autonomy, and discrimination. In recent years, policymakers, civil society advocates, and researchers have proposed a wide range of interventions to address these challenges. In this article, we argue that the emerging toolkit reflects an individualistic view of both personal data and data-driven harms that will likely be inadequate to address growing harms in the global data ecosystem. We maintain that interventions must be grounded in an understanding of the fundamentally collective nature of data, wherein platforms leverage complex patterns of behaviors and characteristics observed across a large population to draw inferences and make predictions about individuals. Using the lens of the collective nature of data, we evaluate various approaches to addressing personalization-driven harms currently under consideration. This lens also allows us to frame concrete guidance for future legislation in this space and advocate meaningful transparency that goes far beyond current proposals. We offer a roadmap for what meaningful transparency must constitute: a collective perspective providing a third party with ongoing insight into the information gathered and observed about individuals and how it correlates with any personalized content they receive-across a large, representative population. These insights would enable the third party to understand, identify, quantify, and address cases of personalization-driven harms. We discuss how such transparency can be achieved without sacrificing privacy and provide guidelines for legislation to support the development of this proposal.
|Original language||American English|
|Title of host publication||CSLAW 2022 - Proceedings of the 2022 Symposium on Computer Science and Law|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||12|
|State||Published - 1 Nov 2022|
|Event||2022 ACM Symposium on Computer Science and Law, CSLAW 2022 - Washington, United States|
Duration: 1 Nov 2022 → 2 Nov 2022
|Name||CSLAW 2022 - Proceedings of the 2022 Symposium on Computer Science and Law|
|Conference||2022 ACM Symposium on Computer Science and Law, CSLAW 2022|
|Period||1/11/22 → 2/11/22|
Bibliographical noteFunding Information:
This work has been supported by a workshop grant from the Sloan Foundation, a gift to the McCourt School of Public Policy and Georgetown University, Simons Foundation Collaboration 733792, and Israel Science Foundation (ISF) grants 1044/16 and 2861/20. This proceedings paper is an abridged version of an article forthcoming in Volume 25, Issue 4, of the Vanderbilt Journal of Entertainment and Technology Law .
© 2022 Owner/Author.
- algorithmic decision making
- echo chamber
- filter bubble