Reducing suicidal risk using artificial intelligence

Rafa, Elean Sugafta and Sase, Takumi (2024) Reducing suicidal risk using artificial intelligence. In: Artificial intelligence and hikmah. Centre for Islamisation & Kulliyyah of Information and Communication Technology, pp. 263-276.

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Abstract

This chapter explores how social media analysis can be used to identify and lessen suicidal behaviour with artificial intelligence (AI) and machine learning. Social media such as Facebook, Twitter, and Reddit have been widely used, and thus, AI may be able to spot linguistic patterns in user posts that are related to suicidal thoughts. AI, as represented by deep learning models, can identify people who are at risk in conjunction with natural language processing (NLP). The chapter also discusses the moral issues that arise when applying AI to such delicate activities, highlighting the significance of accuracy, privacy, and consent. Moreover, it harmonises technological endeavours with the Islamic precept of hikmah, which promotes discernment, mercy, and the conservation of life. This chapter aims to contribute to the reduction of suicide rates and the promotion of mental well-being in a digitally linked society by highlighting the possibilities and constraints of AI-driven suicide prevention through a thorough analysis of existing literature and practical applications.

Item Type:	Book Chapter
Uncontrolled Keywords:	Suicide Risk, AI, Machine Learning, Natural Language Processing
Subjects:	Q Science > Q Science (General) > Q128 Islam and Science Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Dr. Takumi Sase
Date Deposited:	18 Jun 2025 09:47
Last Modified:	18 Jun 2025 09:50
URI:	http://irep.iium.edu.my/id/eprint/121568

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