Algorithmic Transparency and Public Policy: Regulating the Black Box of Artificial Intelligence
Keywords:
Algorithmic transparency, explainable AI (XAI), AI governance, public policy, accountability, ethics in AI, regulatory frameworks, digital governance, black box problem, AI oversight.Abstract
Artificial Intelligence (AI) has become an integral tool for governments and organizations worldwide, shaping decisions in sectors such as healthcare, policing, finance, and welfare distribution. However, the growing influence of AI-based systems brings new challenges to public accountability and transparency. This paper examines the intersection of algorithmic transparency and public policy, exploring how regulatory frameworks can mitigate the “black box” problem of AI decision-making. Through data-driven analysis, policy review, and a case study on predictive policing, the research highlights the importance of explainability, documentation, and ethical oversight in AI governance. Results from professional and citizen-based questionnaires reveal that lack of transparency is the leading factor reducing public trust in AI-driven policies. The study concludes by recommending a governance model that
combines technical transparency with legal and ethical accountability to ensure fair and democratic use of AI.
