Algorithms increasingly influence public decision-making, from healthcare triaging to policing, credit scoring, and welfare distribution. However, opaque computational models—especially machine learning systems—pose ethical, legal, and regulatory challenges due to lack of explainability, biased training data, concealed decision logic, and unaccountable automation. Algorithmic transparency has emerged as a central regulatory demand, supported by global frameworks like the EU Artificial Intelligence Act, GDPR, the U.S. Algorithmic Accountability Act, and OECD AI Principles.
This paper examines the policy dimensions of regulating algorithmic transparency, analyzes trade-offs between transparency, privacy, and innovation, and proposes a multi-layer regulatory model incorporating transparency-by-design, auditing mandates, algorithmic impact assessments, and tier-based compliance. The paper concludes with recommendations for policymakers, regulators, and industry stakeholders, highlighting areas for future research such as global harmonization and governance for autonomous systems.