Influenced speech: machine learning and hate speech

被引:0
|
作者
Javier Jaimes, Federico [1 ,2 ]
机构
[1] Univ Buenos Aires, Filosofia, Buenos Aires, Argentina
[2] IIF SADAF CONICET, Buenos Aires, DF, Argentina
来源
DAIMON-REVISTA INTERNACIONAL DE FILOSOFIA | 2023年 / 90期
关键词
machine learning; hate speech; oppression; algorithmic bias; CONVERSATIONAL EXERCITIVES;
D O I
10.6018/daimon.562091
中图分类号
B [哲学、宗教];
学科分类号
01 ; 0101 ;
摘要
This paper addresses the issue of discriminatory computer programs from the perspective of the philosophy of language. In this discipline, the literature on hate speech has focused its analysis on the effects on oppressed groups. The central idea of the present paper will be to develop a new notion, influenced speech, which will allow us to explain what the oppressor group is led to assert on the basis of systematic oppression. Thus, influenced speech will make it possible both to explain the social reproduction of hate speech and to theoretically frame the discriminatory statements made by the aforementioned computer programs.
引用
收藏
页码:45 / 61
页数:17
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