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
相关论文
共 50 条
  • [21] Correction: Automatic hate speech detection in audio using machine learning algorithms
    Joan L. Imbwaga
    Nagaratna B. Chittaragi
    Shashidhar G. Koolagudi
    International Journal of Speech Technology, 2025, 28 (1) : 313 - 313
  • [22] A comparative analysis of machine learning algorithms for hate speech detection in social media
    Omran, Esraa
    Al Tararwah, Estabraq
    Al Qundus, Jamal
    ONLINE JOURNAL OF COMMUNICATION AND MEDIA TECHNOLOGIES, 2023, 13 (04):
  • [23] Intelligent detection of hate speech in Arabic social network: A machine learning approach
    Aljarah, Ibrahim
    Habib, Maria
    Hijazi, Neveen
    Faris, Hossam
    Qaddoura, Raneem
    Hammo, Bassam
    Abushariah, Mohammad
    Alfawareh, Mohammad
    JOURNAL OF INFORMATION SCIENCE, 2021, 47 (04) : 483 - 501
  • [24] Advances in Machine Learning Algorithms for Hate Speech Detection in Social Media: A Review
    Mullah, Nanlir Sallau
    Zainon, Wan Mohd Nazmee Wan
    IEEE ACCESS, 2021, 9 : 88364 - 88376
  • [25] Multilingual Hate Speech Detection: Innovations in Optimized Deep Learning for English and Arabic Hate Speech Detection
    Hassan AL-Sukhani
    Qusay Bsoul
    Abdelrahman H. Elhawary
    Ziad M. Nasr
    Ahmed E. Mansour
    Radwan M. Batyha
    Basma S. Alqadi
    Jehad Saad Alqurni
    Hayat Alfagham
    Magda M. Madbouly
    SN Computer Science, 6 (3)
  • [26] Hate Speech
    Wagner, A. Jay
    JOURNALISM & MASS COMMUNICATION QUARTERLY, 2023, 100 (02)
  • [27] The hate speech
    Aguilar Pirachican, Manuel Rodrigo
    DESDE EL JARDIN DE FREUD-REVISTA DE PSICOANALISIS, 2019, (19): : 328 - 333
  • [28] Hate Speech
    Gurstein, Rochelle
    SALMAGUNDI-A QUARTERLY OF THE HUMANITIES AND SOCIAL SCIENCES, 2018, (197): : 92 - 104
  • [29] A survey on hate speech detection and sentiment analysis using machine learning and deep learning models
    Subramanian, Malliga
    Sathiskumar, Veerappampalayam Easwaramoorthy
    Deepalakshmi, G.
    Cho, Jaehyuk
    Manikandan, G.
    ALEXANDRIA ENGINEERING JOURNAL, 2023, 80 : 110 - 121
  • [30] Improving Arabic Hate Speech Identification Using Online Machine Learning and Deep Learning Models
    Elzayady, Hossam
    Mohamed, Mohamed S.
    Badran, Khaled
    Salama, Gouda
    PROCEEDINGS OF SEVENTH INTERNATIONAL CONGRESS ON INFORMATION AND COMMUNICATION TECHNOLOGY, ICICT 2022, VOL. 2, 2023, 448 : 533 - 541