Algorithmic transparency is a research concept and practical objective focused on making the processes, inputs, and outputs of algorithmic systems understandable and accessible to relevant stakeholders. This academic concept investigates the internal logic, data dependencies, parameter settings, and operational mechanisms of algorithms, particularly those used in decision-making, prediction, and classification. Key characteristics include the scrutability of algorithmic models, the explainability of their results, and the availability of information about their training data and operational context, enabling external auditing and critical evaluation. Its significance lies in promoting accountability, mitigating bias, ensuring fairness, building trust, and enabling the assessment of the societal impacts of automated systems.