Artificial Intelligence (AI) is transforming societies through its capacity to drive innovation, optimise decision-making, and enhance productivity across diverse sectors. However, the rapid deployment of AI systems raises complex ethical questions that extend beyond technical performance. This review critically examines the ethics of artificial intelligence with emphasis on three central pillars: privacy, fairness, and accountability. AI technologies often rely on vast datasets that risk infringing on individual privacy when mismanaged, necessitating robust frameworks for data governance and consent. Equally pressing is the issue of fairness, as algorithmic bias can perpetuate systemic inequalities and undermine social justice. This concern is particularly acute in sensitive domains such as healthcare, finance, and criminal justice, where biased outputs can have life-altering consequences. Accountability also emerges as a central challenge, as the diffusion of responsibility between developers, organisations, and users creates ambiguity regarding who should be held responsible for harms caused by AI systems. Addressing these ethical dimensions requires an integrated approach that blends technological safeguards with regulatory oversight and societal engagement. The paper explores strategies such as explainable AI, impact assessments, and participatory design as pathways to align innovation with ethical responsibility. Ultimately, the balance between harnessing AI’s transformative potential and safeguarding fundamental rights hinges on continuous dialogue among stakeholders—governments, industry, academia, and civil society. By fostering ethical resilience in AI governance, societies can ensure that innovation does not compromise human dignity, equity, or trust. This work underscores the importance of proactive, interdisciplinary measures to guide the ethical trajectory of AI as it becomes an indispensable part of everyday life.
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Manasseh F. Oguru
Asian Journal of Advanced Research and Reports
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Manasseh F. Oguru (Wed,) studied this question.
www.synapsesocial.com/papers/68d6c68eb1249cec298b2d45 — DOI: https://doi.org/10.9734/ajarr/2025/v19i101169