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All Journal Jurnal Insan Pendidikan dan Sosial Humaniora International Journal of Information Engineering and Science Ignite: Journal Islamic Global Network for Information Technology and Entrepreneurship
Nathanael David Christian Barus
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Humanities Computer Science & IT Education Engineering

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Exploration of Ethics in the Development of Artificial Intelligence: A Literature Review on Social and Moral Implications Nathanael David Christian Barus
Jurnal Insan Pendidikan dan Sosial Humaniora Vol. 2 No. 1 (2024): Februari : Jurnal Insan Pendidikan dan Sosial Humaniora
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/jipsoshum-widyakarya.v2i1.2723

Abstract

This research aims to explore the ethical dimensions in the development of Artificial Intelligence (AI) with a focus on its social and moral implications. The research method involves a literature review to gain a profound understanding of the social and moral impact arising from AI development. Analysis is conducted on various literature sources, including articles, books, and AI-related ethical initiatives. The Future of Life Institute highlights the significant potential of artificial intelligence across various sectors but also underscores significant ethical challenges. This initiative requires careful understanding and handling to ensure that AI development aligns with moral and social values. Ethical issues related to unfair compensation for workers in the 'mechanical turk' industry and the impact of technology companies on human rights and democracy are identified. Furthermore, the research describes ethical issues in the use of personal data to train AI models, emphasizing individual rights regarding trained models and the protection of data subject identities. Additionally, the relationship between humans and robots raises ethical questions about their influence on human values and the potential for violent impacts. This exploration also discusses AI-related ethical initiatives emphasizing human rights, well-being, accountability, and transparency. The Ethically Aligned Design Guidelines from IEEE serve as a primary reference, emphasizing the need for AI development based on ethical principles and human rights. In conclusion, this research underscores the importance of awareness regarding the social and moral implications in AI development. Moral principles such as openness, accountability, justice, security, and freedom serve as guiding principles to ensure that AI provides positive benefits without sacrificing human values.
Literature Review on Histogram-Based Image Forensics for Recaptured Image Detection Nathanael David Christian Barus; Nayem Kibriya; Natasha Fedora Barus
International Journal of Information Engineering and Science Vol. 1 No. 3 (2024): August : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v1i3.35

Abstract

This qualitative literature review explores the realm of histogram-based image forensics for recaptured image detection, addressing the challenges posed by advancements in display technology and the subsequent need for robust forensic techniques. The research methodology involves a systematic approach, including defined research objectives, thorough literature search, data extraction, thematic analysis, and ethical considerations. The focal point is the proposed method utilizing Local Ternary Count (LTC) histograms normalized from residue maps, demonstrating exceptional performance across various databases. The methodology involves residue map calculation, LTC histogram extraction, and experiments showcasing the method's efficiency in both single and mixed databases. The discussion emphasizes emerging frontiers in recaptured image forensics, presenting innovative algorithms categorized by the medium used during the recapture process. The shift towards deep learning methods is noted, with a focus on a proposed algorithm for detecting images recaptured from LCD screens based on quality-aware features and histogram features. The RID field has witnessed advancements, with a detailed overview of methods categorically addressing recapture from LCD screens. Ethical considerations are integrated into the discussion, and the conclusion emphasizes the need for constant adaptation, innovation, and collaboration in the fight against evolving manipulation techniques. Looking ahead, the fusion of features, standardized datasets, and advanced deep learning architectures are identified as key elements for future research in ensuring image authenticity
Development Of Data Security Algorithms: A Literature Review On Information Security In The Context Of Big Data Nathanael David Christian Barus; Natasha Fedora Barus
Journal Islamic Global Network for Information Technology and Entrepreneurship Vol. 2 No. 1 (2024): January : Journal Islamic Global Network for Information Technology and Entrepr
Publisher : STIKes Ibnu Sina Ajibarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59841/ignite.v2i1.933

Abstract

In the era of Big Data, securing sensitive information and ensuring data integrity have become paramount concerns due to the unprecedented volume and intricacy of data. Traditional security algorithms face significant challenges in adapting to the distinct characteristics of Big Data. This literature review explores the evolution of data security algorithms tailored explicitly for the Big Data landscape, aiming to address the increasing demand for robust security solutions capable of handling the unique challenges posed by the massive scale and complexity of data. By scrutinizing existing literature, the review unveils advancements, trends, and innovations developed by researchers and practitioners to mitigate vulnerabilities associated with handling vast datasets. The review also sheds light on emerging technologies and cryptographic techniques specifically designed for Big Data security, contributing to enhanced confidentiality, integrity, and availability in the face of evolving cyber threats. While these developments offer advantages such as improved data protection and threat detection, the review highlights challenges, including algorithmic bias, computational complexity, privacy trade-offs, and a shortage of skilled workforce. By considering these factors and emphasizing continuous improvement and ethical considerations, organizations can responsibly leverage data security algorithms to enhance information security in the era of Big Data.
Co-Authors Natasha Fedora Barus Nayem Kibriya