Legal Risk Regulation of Personalized Recommendation Algorithms

XIE Yongjiang; YANG Yongxing; LIU Tao

doi:10.19979/j.cnki.issn10082689.2022110031

Volume 40 Issue 1

Feb. 2024

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Article Navigation > Journal of University of Science and Technology Beijing ( Social Sciences Edition) > 2024 > 40(1): 77-85

XIE Yongjiang, YANG Yongxing, LIU Tao. Legal Risk Regulation of Personalized Recommendation Algorithms[J]. Journal of University of Science and Technology Beijing ( Social Sciences Edition), 2024, 40(1): 77-85. doi: 10.19979/j.cnki.issn10082689.2022110031

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XIE Yongjiang, YANG Yongxing, LIU Tao. Legal Risk Regulation of Personalized Recommendation Algorithms[J]. Journal of University of Science and Technology Beijing ( Social Sciences Edition), 2024, 40(1): 77-85. doi: 10.19979/j.cnki.issn10082689.2022110031

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Legal Risk Regulation of Personalized Recommendation Algorithms

doi: 10.19979/j.cnki.issn10082689.2022110031

Institute of Internet Governance and Law, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received Date: 2022-11-07
Available Online: 2024-01-04
Publish Date: 2024-02-25

Abstract

Abstract

Information overload has led to the rise of personalized recommendation algorithm for information distribution. Personalized recommendation algorithms, while solving problems of information overload and long tail, meeting users’ personalized needs, and improving the efficiency of Internet information service, have also triggered many legal risks such as the interference with with users’ self-generated meaning, privacy leakage, information cocoon, and algorithm discrimination. The mission of jurisprudence is not to praise the achievements of personalized recommendation algorithms, but to examine their potentially irrational consequences, and to explore how to reduce the risks possibly caused by the development of algorithmic recommendations through the rule of law. To this end, a responsive algorithm regulation should establish an open view of privacy protection, strengthen the obligation of algorithm notification and the right of algorithm application rejection, improve the right of algorithm interpretation, and build an algorithm audit system to reduce the legal risks brought by personalized recommendation algorithms under principles of integrity, autonomy, justice and proportionality.
- personalized recommendations,
- algorithm,
- legal risks,
- legal regulation

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References(34)

References

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