A COMPREHENSIVE REVIEW OF BIAS IN AI ALGORITHMS

Abdul Wajid Fazil; Musawer Hakimi; Amir Kror Shahidzay

doi:10.59003/nhj.v3i8.1052

Authors

Abdul Wajid Fazil Badakhshan University
Musawer Hakimi Samangan University
Amir Kror Shahidzay Kabul University

DOI:

https://doi.org/10.59003/nhj.v3i8.1052

Keywords:

Algorithmic Bias, Literature Synthesis, Mitigation Strategies, Industry Implications, Ethical AI Deployment

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.

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