International Journal of Scientific Research and Engineering Development

The rapid spread of AI systems across sectors has raised new ethical and fairness challenges for researchers, developers, and policy-makers. Modern AI ethics entails considerations of algorithmic bias, fairness metrics, frameworks for governance, your mechanisms for transparency, and methodologies for interdisciplinary collaboration. This in-depth literature review highlights the growth of AI ethics from theoretical calls to action through practical and implementation challenges, and considers nearly a decade of perspectives on bias detection and fairness operationalization and implementation and accountable AI. The review addresses new and ongoing challenges for ethical AI, such as algorithmic discrimination, data representation, cultural bias in global AI design, and other challenges, while also presenting the emerging solutions such as explainable AI, stakeholders engagement, and adaptive frameworks for governance. Finally, this review discusses more recent directions, such as foundation models for ethical AI, principles of human-centered design, and regulatory compliance frameworks that are now shaping the next generation of responsible artificial intelligence systems.