Decision-making is a structured process involving the identification of objectives, the collection of relevant information, and the evaluation of potential solutions. This process fosters an environment conducive to innovation. Artificial Intelligence (AI) and Machine Learning (ML) algorithms comprise a set of instructions that enable machines to learn and make decisions based on acquired knowledge. The rapid evolution of research, development, and application of these technologies has led to their increasing integration into decision-making processes. To ensure the ethical use of AI and ML algorithms, comprehensive guidelines have emerged. These guidelines provide a framework for the responsible development and deployment of technology. By incorporating principles of transparency, fairness, accountability, and accuracy into AI and ML algorithms, these guidelines aim to build trust and mitigate bias. Despite the existence of various guidelines, they often lack specific applicability to particular use cases. To address these challenges and provide practical guidance, we have derived actionable guidelines for the ethical use of AI and ML algorithms in decision-making from existing ethics. This review paper analyzes these guidelines and provides a detailed overview of the ethical principles underpinning them.