The Digital Turn: AI, Algorithmic Tools, Big Data, and Integrity Risks

Abstract

This panel explores how the rapid advancement of digital technologies—including artificial intelligence (AI), machine learning, and large-scale data infrastructures—is transforming both the measurement of corruption and the design of anticorruption interventions. The growing availability of big data, digital footprints, and algorithmic analytics is enabling new approaches to detecting, mapping, and predicting corruption risks across sectors and levels of governance. These tools allow researchers and policymakers to move beyond perception-based indicators toward more granular, real-time, and behaviourally grounded measures of corrupt practices. At the same time, the panel recognises that the use of big data and algorithmic systems introduces significant risks and normative challenges that require careful scrutiny. These include issues of data quality and bias, opacity and explainability of algorithms, unequal access to digital resources, privacy and surveillance concerns, and the potential for automated systems to reinforce existing power asymmetries or be weaponized for political control. By foregrounding both the opportunities and the limitations of data-driven anticorruption tools, the panel aims to foster a balanced and critical discussion on how emerging technologies can be responsibly integrated into anticorruption research and policy without undermining democratic values or institutional integrity. The panel brings together theoretical and empirical contributions from a range of interdisciplinary perspectives. It also offers critical analyses of the development, adoption, and procurement of AI-based solutions, as well as the use of algorithmic monitoring and decision-support tools in anticorruption efforts, assessing their implications for transparency, due process, public accountability, and institutional ethics.

Title	Details
Governing the Data Backbone: Designing and Deploying Multi-Actor Nodes for AI-Driven Anti-Corruption Policy	View Paper Details
The Paradox of AI-Driven Anti-Corruption in the Public Sector: New Corruption Risks in AI Procurement	View Paper Details
Measurement of Corruption with AI: Beyond Hype, Toward Evidence and Enduring Statistical Challenges	View Paper Details
Automating Integrity: The Promise and Limits of LLMs for Anti-Corruption Knowledge and Policy	View Paper Details
From Monitoring to Evaluation: Using Red Flag Indicators to Assess Systemic Change in Public Procurement	View Paper Details