Algorithmic Governance and Public Accountability: Audits, Transparency, and Harm

Tarcisius Niwagaba

Humanities Education Kampala International University Uganda

Email: tarcisius.niwagaba@kiu.ac.ug

ABSTRACT

Algorithmic systems are increasingly embedded in public administration, shaping decisions in areas such as social services, law enforcement, urban governance, and financial regulation. While these systems promise efficiency, scalability, and data-driven insights, their deployment also raises significant concerns regarding transparency, fairness, discrimination, and accountability. This paper examines the governance of algorithmic systems through three central mechanisms: audits, transparency, and harm assessment. It explores how algorithmic governance frameworks can ensure the responsible deployment of artificial intelligence (AI) systems while safeguarding public trust and democratic values. The study reviews mechanisms of internal and external audits, emphasizing the importance of standardized auditing procedures, performance metrics, and independent oversight to evaluate algorithmic performance and detect biases or unintended consequences. It further analyzes transparency practices, including data provenance, model disclosure, explainability, and governance records, which enable citizens, regulators, and stakeholders to understand and scrutinize automated decision-making processes. In addition, the paper discusses various forms of harm arising from algorithmic systems, ranging from individual-level discrimination and privacy violations to systemic risks that undermine democratic institutions and social cohesion. Drawing on international case studies from municipal governments and public service delivery contexts, the paper highlights both the opportunities and limitations of current governance approaches. It concludes that effective algorithmic governance requires multi-layered accountability structures involving policymakers, public institutions, industry actors, and civil society. Strengthening auditing standards, enhancing transparency, and establishing robust mechanisms for harm mitigation and redress are essential to ensure that algorithmic systems operate in ways that promote fairness, protect human rights, and uphold democratic accountability.

Keywords: Algorithmic Governance, Public Accountability, Algorithmic Audits, Transparency and Explainability, and Algorithmic Harm.

CITE AS: Tarcisius Niwagaba (2026). Algorithmic Governance and Public Accountability: Audits, Transparency, and Harm. NEWPORT INTERNATIONAL JOURNAL OF CURRENT ISSUES IN ARTS AND MANAGEMENT, 7(1): 10-19. https://doi.org/10.59298/NIJCIAM/2025/71.1019