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循环、阶梯与链接:社会与机器学习的递归性

Loops, ladders and links: the recursivity of social and machine learning.

作者信息

Fourcade Marion, Johns Fleur

机构信息

Department of Sociology, University of California Berkeley, 410 Barrows Hall, Berkeley, CA 94720-1980 USA.

UNSW Law, UNSW Sydney, Sydney, NSW 2052 Australia.

出版信息

Theory Soc. 2020;49(5-6):803-832. doi: 10.1007/s11186-020-09409-x. Epub 2020 Aug 26.

Abstract

Machine learning algorithms reshape how people communicate, exchange, and associate; how institutions sort them and slot them into social positions; and how they experience life, down to the most ordinary and intimate aspects. In this article, we draw on examples from the field of social media to review the commonalities, interactions, and contradictions between the dispositions of people and those of machines as they learn from and make sense of each other.

摘要

机器学习算法重塑了人们沟通、交流和交往的方式;重塑了机构对人们进行分类并将他们置于社会地位的方式;还重塑了人们体验生活的方式,甚至涉及到最普通和私密的方面。在本文中,我们借鉴社交媒体领域的例子,来审视人与机器在相互学习并理解彼此的过程中,二者倾向之间的共性、互动和矛盾。

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