Difference between revisions of "Erickson and Kretschmer (2019)"
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|Method of Analysis=Qualitative Coding / Sorting (e.g. of interview data) | |Method of Analysis=Qualitative Coding / Sorting (e.g. of interview data) | ||
|Industry=Publishing of books, periodicals and other publishing; Software publishing (including video games); Film and motion pictures; Television programmes; Sound recording and music publishing; Creative, arts and entertainment | |Industry=Publishing of books, periodicals and other publishing; Software publishing (including video games); Film and motion pictures; Television programmes; Sound recording and music publishing; Creative, arts and entertainment | ||
− | |Country= | + | |Country=Europe;Global;United States;European Union |
|Cross-country=Yes | |Cross-country=Yes | ||
|Comparative=No | |Comparative=No |
Revision as of 09:30, 20 April 2020
Contents
Source Details
Erickson and Kretschmer (2019) | |
Title: | Empirical Approaches to Intermediary Liability |
Author(s): | |
Year: | 2019 |
Citation: | Erickson, K. And Kretschmer, M. (2019) Empirical Approaches to Intermediary Liability in THE OXFORD HANDBOOK OF INTERMEDIARY LIABILITY ONLINE (ed. Giancarlo Frosio), Oxford University Press, Forthcoming |
Link(s): | Open Access |
Key Related Studies: | |
Discipline: | |
Linked by: |
About the Data | |
Data Description: | The study consists of a literature review of empirical studies relating to notice-and-takedown systems. Drawing on an initial sample of empirical studies featured on the Copyright Evidence Wiki, the study then uses a snowball sampling method to identify further published research. |
Data Type: | Secondary data |
Secondary Data Sources: | |
Data Collection Methods: | |
Data Analysis Methods: | |
Industry(ies): | |
Country(ies): | |
Cross Country Study?: | Yes |
Comparative Study?: | No |
Literature review?: | No |
Government or policy study?: | No |
Time Period(s) of Collection: |
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Funder(s): |
Abstract
“Legal theory has failed to offer a convincing framework for the analysis of the responsibilities of online intermediaries. The debate is characterised by a wide range of contested issues. This paper considers what empirical evidence may contribute to these debates. What do we need to know in order to frame the liability of intermediaries and, a forteriori, what does the relationship between theory and empirics imply for the wider issue of platform regulation? The core of the paper is a systematic review of existing empirical research on the copyright liability regime established with the Digital Millennium Copyright Act (DMCA 1998) and the EU E-Commerce Directive (2000). Issues examined include the number and accuracy of takedown notices, over-enforcement and abuse, transparency and due process, and finally the allocation of responsibilities and costs.”
Main Results of the Study
Based on a review of available literature, there are five key strands of empirical inquiry into notice-and-takedown systems: the volume of takedown requests, the accuracy of notices, the potential for over-enforcement or abuse, transparency of the takedown process, and the costs of enforcement borne by different parties.
Having surveyed the literature available, the study concludes that “despite its flaws, the notice-and-takedown regime is working. Rightholders make effective use of the notice-and-takedown system which has dramatically accelerated with the use of automated systems since about 2012. The potential for abuse, while real, it likely over-stated. The distribution of cost burdens creates incentives for rightholders to pursue instances of straight piracy, while user-generated re-use remains largely tolerated.
Policy Implications as Stated By Author
The study recommends “tweaking, rather than overhauling” the notice-and-takedown regime. Improvements may be made by rightsholders and platforms in regards accountability and transparency in order to promote due process. Delegating regulatory functions (such as filtering and monitoring) to platforms themselves remains risky due to a lack of oversight over e.g. platforms AI and machine learning processes.
Coverage of Study
Datasets
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