Working Papers

M. Goos, M. Savona – The governance of artificial intelligence: Harnessing opportunities and mitigating challenges

The OECD defines an AI (Artificial Intelligence) system as “a machine-based system that can influence the environment by producing an output (predictions, recommendations, or decisions) for a given set of objectives. It uses machine and/or human-based data and inputs to (i) perceive real and/or virtual environments; (ii) insert these perceptions into models through analysis in an automated manner (e.g., with machine learning), or manually; and (iii) use model inference to formulate options for outcomes. AI systems are designed to operate with varying levels of autonomy” (OECD 2019).

This definition relates AI to the type of technology that has created the recent excitement around technological progress: machine learning. Machine learning is a branch of computational statistics that focuses on designing algorithms to make predictions from new data without explicitly programming the solution. Since 2012, the use of machine learning as a prediction technology has grown substantially. Machine learning is now commonplace: Pandora learns how to make better music recommendations based on its users’ preferences; Google learns how to automatically translate content into different languages based on translated documents found online; and Facebook learns how to identify people in photos based on its database of known users.

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