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Disruptive AI technology and hate speech: A legal redress in Malaysia

Mohd Saufi, N.N.
; 
Kamaruddin, S.
; 
Mohammad, A.M.
; 
Jabar, N.A.A.
; 
Wan Rosli, Wan R.
; 
Talib, Z.M.
Publication Date
2023-06
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Keywords
Ethics, Privacy, Law, Social networking (online), Hate speech, Legislation, Artificial intelligence
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Peer-Reviewed
Yes
Open Access status
closedAccess
Accepted for publication
2023
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Management and Law Publications
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Abstract
Artificial intelligence (AI) technology is becoming increasingly prevalent in society, offering a range of benefits and opportunities. However, with the rise of AI comes new challenges, particularly in hate speech. Hate speech, a type of expression that incites hatred or violence against individuals or groups based on ethnicity, religion, or other characteristics, has become a growing concern in Malaysia, with social media and online platforms becoming a breeding ground for such speech. In this context, AI technology has emerged as a potential solution for monitoring and regulating hate speech, but it also presents legal and ethical challenges that must be addressed. In view of double edge sword roles played by the development of AI, this article analyses the legal recourse available in Malaysia for disruptive AI technology and hate speech. The authors claim that AI systems are prone to errors and biases and that there is a risk of relying too much on such plans at the expense of human judgement. There are also concerns regarding the impact of AI on free expression and privacy rights. In addition, the author suggests that artificial intelligence be appropriately regulated to ensure that it is consistent with international human rights standards and national laws.
URI
http://hdl.handle.net/10454/19622
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Citation
Mohd Saufi NN, Kamaruddin S, Mohammad AM et al (2023) Disruptive AI technology and hate speech: A legal redress in Malaysia. In: 2023 International Conference on Disruptive Technologies (ICDT). 11-12 May 2023, Greater Noida, India. 759-763.
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Link to Version of Record
https://doi.org/10.1109/ICDT57929.2023.10150942
Type
Conference paper
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