Procurement Corruption and Artificial Intelligence: Between the Potential of Enabling Data Architectures and the Constraints of Due Process Requirements

Abstract

This contribution argues that the expectations around the deployment of AI as an anti-corruption tool in procurement need to be tamed. It explores how the potential applications of AI replicate anti-corruption interventions by human officials and, as such, can only provide incremental improvements but not a significant transformation of anti-corruption oversight and enforcement architectures. It also stresses the constraints resulting from existing procurement data and the difficulties in creating better, unbiased datasets and algorithms in the future, which would also generate their own corruption risks. The contribution complements this technology-centred analysis with a critical assessment of the legal constraints based on due process rights applicable even when AI supports continued human intervention. This in turn requires a close consideration of the AI-human interaction, as well as a continuation of the controls applicable to human decision-making in corruption-prone activities. The contribution concludes, first, that prioritising improvements in procurement data capture, curation and interconnection is a necessary but insufficient step; and second, that investments in AI-based anti-corruption tools cannot substitute, but only complement, current anti-corruption approaches to procurement.