Authors : Muhammad Yaaseen Hossenbux

Volume/Issue : Volume 11 - 2026, Issue 2 - February

Google Scholar : https://tinyurl.com/44nst3b7

Scribd : https://tinyurl.com/3scwjyxs

DOI : https://doi.org/10.38124/ijisrt/26feb725

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Artificial intelligence (AI) is increasingly embedded within high-stakes human decision systems, including medical diagnostics, judicial decision-making, financial forecasting, and autonomous control systems. While these technologies promise improved efficiency, accuracy, and scalability, their growing authority over life-critical and socially consequential decisions introduces significant ethical, legal, and systemic risks. This paper presents a comprehensive risk analysis of artificial intelligence in high-stakes decision environments through a structured synthesis of interdisciplinary literature. The study identifies six major risk categories: algorithmic opacity, data confidentiality vulnerabilities, automation bias, ethical displacement, systemic fragility, and adversarial manipulation. The analysis demonstrates that as AI systems assume greater decision-making autonomy, human oversight is progressively reduced, increasing exposure to unpredictable failures, biased outcomes, and moral misalignment. Furthermore, the interconnected nature of modern socio-technical infrastructures amplifies these risks, enabling localized algorithmic errors to propagate across institutional and societal systems. To address these challenges, the paper proposes a conceptual mitigation framework emphasizing transparency, human-centred oversight, ethical governance, and regulatory alignment. Understanding and proactively managing these risks is essential to ensure that artificial intelligence enhances human decision-making without undermining accountability, trust, and social stability.

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