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All Journal Nusantara Hasana Journal Jurnal Ilmiah Multidisiplin Indonesia
Abdul Wajid Fazil
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Nursing Physics

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Human factors in cybersecurity: an in depth analysis of user centric studies Hakimi, Musawer; Mohammad Mustafa Quchi; Abdul Wajid Fazil
Jurnal Ilmiah Multidisiplin Indonesia (JIM-ID) Vol. 3 No. 01 (2024): Jurnal Ilmiah Multidisiplin Indonesia (JIM-ID), January
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/esaprom.v3i01.3832

Abstract

This study delves into the intricate intersection of human behavior, cognition, and technology within the cybersecurity domain, aiming to enhance our understanding of the human-centric challenges influencing the effectiveness of cybersecurity measures. The primary objective is to unravel the nuanced landscape where human errors persist as a significant contributing factor to security breaches, emphasizing the need for a holistic comprehension of human factors. The study recognizes the evolving nature of work, with an increasing number of individuals operating from home, and the consequential challenges in managing human factors in the digital era. The blurring lines between private and public lives, coupled with the rise of social credit systems, necessitate a thorough examination of key elements intersecting with cybersecurity Employing a systematic literature review, this research methodically identifies, filters, and analyzes pertinent literature concerning human-centric factors in cybersecurity. The systematic approach involves the formulation of specific research questions guiding the study, strategic search plans targeting reputable databases, and meticulous study selection processes based on predefined criteria The study unfolds through a series of interconnected research questions, addressing the impact of human factors on operational efficiency, challenges in the adoption of human-centric approaches, and the ways in which human factors influence strategic decision-making in cybersecurity. The results shed light on the substantial contribution of understanding user behavior and cognitive processes to the development of tailored cybersecurity strategies. Challenges, such as security fatigue and the scarcity of psychology-based professionals, are addressed, advocating for human factors engineering and strategic initiatives to enhance education and training programs. In conclusion, embracing a human-centric paradigm emerges as imperative for organizations striving to fortify their defenses against dynamic and sophisticated cyber threats. Integrating technology with a profound understanding of human factors becomes the cornerstone for shaping a resilient and adaptive cybersecurity future.
A COMPREHENSIVE REVIEW OF BIAS IN AI ALGORITHMS Abdul Wajid Fazil; Musawer Hakimi; Amir Kror Shahidzay
Nusantara Hasana Journal Vol. 3 No. 8 (2023): Nusantara Hasana Journal, January 2024
Publisher : Yayasan Nusantara Hasana Berdikari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59003/nhj.v3i8.1052

Abstract

This comprehensive review aims to analyze and synthesize the existing literature on bias in AI algorithms, providing a thorough understanding of the challenges, methodologies, and implications associated with biased artificial intelligence systems.Employing a narrative synthesis and systematic literature review approach, this study systematically explores a wide array of sources from prominent databases such as PubMed, Google Scholar, Scopus, Web of Science, and ScienceDirect. The inclusion criteria focused on studies that distinctly defined artificial intelligence in the education sector, were published in English, and underwent peer-review. Five independent reviewers meticulously evaluated search results, extracted pertinent data, and assessed the quality of included studies, ensuring a rigorous and comprehensive analysis. The synthesis of findings reveals pervasive patterns of bias in AI algorithms across various domains, shedding light on the nuanced aspects of discriminatory practices. The systematic review highlights the need for continued research, emphasizing the intricate interplay between bias, technological advancements, and societal impacts. The comprehensive analysis underscores the complexity of bias in AI algorithms, emphasizing the critical importance of addressing these issues in future developments. Recognizing the limitations and potential consequences, the study calls for a concerted effort from researchers, developers, and policymakers to mitigate bias and foster the responsible deployment of AI technologies. Based on the findings, recommendations include implementing robust bias detection mechanisms, enhancing diversity in AI development teams, and establishing transparent frameworks for algorithmic decision-making. The implications of this study extend beyond academia, informing industry practices and policy formulations to create a more equitable and ethically grounded AI landscape.
Co-Authors Amir Kror Shahidzay Hakimi, Musawer Mohammad Mustafa Quchi Musawer HAKIMI