Data-Driven, Information-Enabled Regulatory Delivery

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Industries and businesses are becoming increasingly digital, and the COVID-19 pandemic has further accelerated this trend. Regulators around the world are also experimenting with data-driven tools to apply and enforce rules in a more agile and targeted way. This report maps out several efforts undertaken jointly by the OECD and Italian regulators to develop and use artificial intelligence and machine learning tools in regulatory inspections and enforcement. It provides unique insights into the background processes and structures required for digital tools to perform predictive modelling, risk analysis and classification. It also highlights the challenges such tools bring, both in specific regulatory areas and to the broader goals of regulatory systems.


Applying machine learning techniques to inspections

This chapter details the manner in which IT tools have been used to manage occupational safety and health inspections in construction sites in Lombardy as well as food safety inspections in the Campania region. Brief mention is also made to other OECD projects involving predictive systems for assessing water quality and self-certification tools. These projects illustrate the moving away from the systematic use of data in revenue-based regulatory areas to other domains where data has been historically difficult to manage. The chapter adds the following key observations: i) Machine Learning can help predict indicators of risk; ii) Artificial Intelligence systems can help improve enforcement performance through quantitative analysis of inspections, and; iii) conventional assumptions of the best predictor of non-compliance are being challenged through Artificial Intelligence systems.


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