ISLAMIC ETHICS OF ALGORITHMIC BIAS: A FRAMEWORK FOR FAIRNESS IN MACHINE LEARNING APPLICATIONS
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AI Governance, Algorithmic Bias, Machine Learning Fairness
Abstract
The expanding use of machine learning systems in socially consequential domains has intensified concerns about algorithmic bias, particularly in contexts where ethical legitimacy must align with religious and cultural values. The absence of frameworks that integrate computational fairness with Islamic moral philosophy presents a critical gap in current AI ethics discourse. This study aims to construct an Islamic ethical framework for evaluating and mitigating algorithmic bias by systematically mapping core Islamic principles—justice, harm prevention, accountability, and human dignity—onto established fairness methodologies in machine learning. A qualitative conceptual design was employed, utilizing structured content analysis of classical and contemporary Islamic ethical texts alongside interdisciplinary AI fairness literature. The findings reveal substantial conceptual convergence between Islamic ethics and technical fairness approaches, demonstrating that Islamic moral constructs can function as normative foundations for bias assessment. The study concludes that integrating Islamic ethics into AI governance offers a culturally grounded and ethically robust model capable of enhancing fairness evaluation and strengthening public trust in algorithmic decision systems. The proposed framework contributes to the emerging field of non-Western AI ethics and provides a basis for developing practical guidelines for ethical AI implementation in Muslim-majority contexts.
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References
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Copyright (c) 2025 Rustiyana Rustiyana, Fatimah Al-Rashid, Fatima Malik
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