Table 1: G7 Compliance Simulator’s predictions of outcomes for the Apulia Summit by subject
Gender Trade Crime and corruption Democracy Development Food and agriculture
Human rights Nuclear safety Regional security UN reform Education Terrorism Climate change
Conflict prevention Financial regulation Infrastructure Labour and employment Macroeconomics Migration and refugees Health Environment Nonproliferation Energy Social policy ICT and digitalisation International cooperation
JESSICA RAPSON Jessica Rapson is a senior researcher at the G7 and G20 Research Groups and a Master of Statistical Science candidate at the University of Oxford. She is also a graduate of University of Toronto’s Munk School of Global Affairs and Public Policy. Her work focuses on building statistical models for use in policy analysis and algorithmic governance.
X-TWITTER @g7_rg www.g7.utoronto.ca
during gatherings, allowing leaders to allocate additional discussion and efforts to strategise ways to meet these commitments. PREDICTING OUTCOMES FOR THE 2024 APULIA SUMMIT Table 1 shows the G7 Compliance Simulator’s predictions of outcomes for the Apulia Summit by subject. These predictions are based only on scheduled ministerial meetings and subjects for the commitments, as the specific content of the summit commitments is not available until after the summit itself. On the eve of the summit, commitments on gender are predicted to have the worst outcomes, and commitments on trade, crime and corruption, democracy, development, food and agriculture, human rights, nuclear safety, regional security and reform of the United Nations are predicted to have subpar performance. Similarly, Table 2 shows the G7 Compliance Simulator’s general predictions of outcomes for the Apulia Summit by member. Italy and Japan are predicted to be the least likely to meet their commitments. These predictions are not intended to cast a negative judgement on members deemed
at risk of not fulfilling their commitments. Rather, these assessments rely on historical trends observed in adhering to commitments made at G7 summits. In fact, highlighting members with historically lower compliance rates as at risk might even serve as a catalyst for directing resources to support their improvement. CONCLUSION AI tools with predictive capabilities, like the G7 Compliance Simulator, have the potential to amplify the influence of the G7. By promptly identifying high-risk commitments through real-time prediction of outcomes during summits, these tools enable targeted resource allocation to areas with lower predicted success probabilities. With the integration of predictive AI, G7 summits can be more effectively positioned to address global challenges strategically. Note: The compliance simulator can be accessed at g7-utoronto.ca/shinyapps. io/compliance-tool/. Full data and code are available at github.com/rapsoj/ g7-compliance.
Table 2: G7 Compliance Simulator’s predictions of outcomes for the Apulia Summit by member
Italy Japan France Canada Germany United States
United Kingdom European Union
Probability of meeting commitments (%)
50%–60% 60%–70% 70%–80% 80%–90%
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2024 — G7 ITALY: THE APULIA SUMMIT
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