Algorithmic Accountability and Anti-Corruption Governance: A Framework for Detecting Substandard and Falsified Medical Products

The proliferation of substandard and falsified (SF) medical products represents a critical failure of pharmaceutical governance that disproportionately harms populations in low- and middle-income countries. Criminological research has long documented the structural conditions that enable pharmaceutical corruption, from corporate crime in drug testing and manufacturing to the transnational networks that traffic falsified medicines across regulatory boundaries. While international anti-corruption instruments, notably the United Nations Convention Against Corruption (UNCAC) and the Council of Europe MEDICRIME Convention, provide legal frameworks to combat pharmaceutical corruption, significant enforcement gaps persist. Algorithmic decision-making systems are increasingly deployed to detect SF products within pharmaceutical supply chains, yet these systems operate largely outside established accountability frameworks and may themselves reproduce the structural biases that characterise global pharmaceutical markets. This article proposes an Algorithm Accountability Benchmark (AAB) designed to evaluate the transparency, fairness, and accountability of algorithms used in pharmaceutical product detection and regulatory enforcement. Drawing on the theoretical frameworks of criminogenic asymmetries, global anomie and dysnomie, and the concept of practices that are “lawful but awful,” the paper argues that algorithmic governance tools must be assessed not only for technical performance but also for their capacity to either reinforce or disrupt the structural conditions that enable pharmaceutical corruption. The AAB integrates anti-corruption governance principles derived from UNCAC with algorithmic accountability standards from the European Union Artificial Intelligence Act, offering a comprehensive evaluation methodology across prevention, detection, reporting, and enforcement dimensions. The article contributes to the growing interdisciplinary literature linking anti-corruption law, algorithmic governance, and global health security.