With the emergence of big data analytics, computational power, and breakthroughs of algorithmic models, artificial intelligence (AI) has been developing rapidly. Among others, the “algorithmization” of government functions holds the potential to improve overall efficiency, but at the same time, it also poses adverse impact on fundamental rights due to inadequate data protection, systematic bias and discrimination, algorithmic opacity, and infringement on fairness and due process rights. As a result, the AI ethics principles, government or corporate AI policies, and other instruments in recent years have highlighted “transparency” as one of the core governance principles. Yet what is transparency? Do we require different levels of transparency for different types of decision-making and power wielding? What does the so-called transparency principle entail in legal and policy terms? This talk aims to explore together the definition, types, levels, and normative substance of transparency in broader contexts of algorithmic governance, which have been lacking clear and full understanding or discourse.  By examining and articulating the normative functions, typology, values, and limits of the transparency principle, this talk will endeavors to point out the blind spots of the existing discourse, how to address them, and the way forward.