Constructing an Ex Ante Necessary Measures Framework for Copyright Infringement in Algorithmic Recommendation Services

Abstract

Algorithmic recommendation is now the dominant way of distributing content. Infringing contents are easily spread by algorithmic means. Traditional "notice-and-takedown" rule and general provisions regarding necessary measures fail to deal with algorithmic context, leading to great divergence in judicial practice. Academic discussion focuses only on whether it is legitimate to establish ex ante preventive obligations without developing any systematic criteria for its application. Ex ante necessary measures represent a private law duty of care incumbent upon platforms different from those based on public law review obligation. Algorithmic recommendation platforms shall be considered technical service providers; algorithmic intervention only changes the intensity of their duties instead of changing their status as service provider. Good-faith preventive rules should also be introduced to relieve platform's concern about the dilemma that "greater efforts may lead to greater liability." Typological construction of ex ante measures should be determined according to technical features of algorithms, control capability of platforms, risk of copyright infringement, and the principle of proportionality. Clarification of the boundary of preventive obligations will enable us to set up a hierarchy of ex ante preventive measures: automated copyright filtering, differentiated tiered preliminary review, algorithmic distribution permission control and supportive compliance mechanisms. Together they form a complete chain of ex ante infringement prevention. This approach follows the principle of technological neutrality and prohibits general and comprehensive monitoring. It helps harmonize judicial standards, balance copyright protection and freedom of information, improve the application of safe harbor rules in algorithmic contexts.