<![CDATA[Artificial intelligence (AI)-based algorithmic decision-making is a major concern in the digital age due to its potential to improve efficiency in various sectors, including healthcare, law, and finance. However, its implementation poses significant ethical challenges, such as bias in data and a lack of transparency that can affect fairness and public trust. This research aims to explore ethical considerations in algorithmic decision-making with a focus on fairness and transparency, identify key challenges, and provide policy recommendations to improve the accountability of AI systems. The research method uses a qualitative approach through literature studies that include academic articles, books, and research reports. The results show that algorithmic bias often arises due to unrepresentative historical data, while low transparency makes it difficult to understand the decision-making process. To overcome this problem, independent algorithm audits, the application of explainable AI, progressive regulations, public education, and the use of more diverse data are needed. This recommendation aims to create a fair, transparent, and trustworthy AI system, thereby supporting wider acceptance of the technology in society.]]>
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