study purpose

decision_tree <- rpart(Political.regime ~ Talent + Infrastructure + AI_Capability_Index + Public_Sector_Innovation, data = df, method = "class") # Plot the decision tree rpart.plot(decision_tree, type = 2, extra = 104, under = TRUE, faclen = 0, cex = 0.8)

Our team, Ctrl+Alt+Defeat, is excited to participate in this year’s Women in Data: Datathon 2024 with a project focused on generative AI and its role in supporting reproductive health decisions. As a team composed of individuals who have experienced menstruation, we understand the critical need for the 1.8 billion people worldwide who menstruate and approximately 1.2 billion people who are menopausal or postmenopausal to have access to reliable and unbiased information. Our research aims to uncover biases in generative AI and identify fairness gaps affecting inclusivity.

Autores: Xavi Stevn Restrepo Montoya Juan Esteban Londoño Bejarano Laura Sofia Giraldo Bolaños Jeffer Cabezas Sevillano

# Add custom annotations to describe the clusters ggplot(hclust.project2D, aes(x = PC1, y = PC2)) + geom_point(aes(shape = cluster_label, color = cluster_label), size = 3) + geom_text(aes(label = country, color = cluster_label), hjust = 0, vjust = 1, size = 3) + geom_polygon(data = hclust.hull, aes(group = cluster, fill = as.factor(cluster)), alpha = 0.4, linetype = 0) + theme_minimal() + labs(title = "Geopolitical Decisions Based on AI Capabilities", x = "Principal Component 1 (AI Capability)", y = "Principal Component 2 (Government Strategy)", color = "Cluster", shape = "Cluster") + scale_fill_manual(values = c("#FF9999", "#99CCFF", "#66CC66"), labels = c("AI Leaders", "Emerging AI Players", "Tech Adopters")) + theme(text = element_text(size = 16)) + annotate("text", x = 5, y = -2, label = "AI Leaders: Advanced AI capabilities, \nForm AI alliances, Lead AI standards", color = "#FF9999", size = 5, hjust = 0) + annotate("text", x = -4, y = 3, label = "Emerging AI Players: Focus on AI regulation, \nBuild partnerships for growth", color = "#99CCFF", size = 5, hjust = 0) + annotate("text", x = -6, y = -5, label = "Tech Adopters: Focus on cybersecurity, \nEnsure digital sovereignty", color = "#66CC66", size = 5, hjust = 0)

library(ggplot2) # Plot the clusters with meaningful labels ggplot(hclust.project2D, aes(x = PC1, y = PC2)) + geom_point(aes(shape = cluster_label, color = cluster_label), size = 3) + geom_text(aes(label = country, color = cluster_label), hjust = 0, vjust = 1, size = 3) + geom_polygon(data = hclust.hull, aes(group = cluster, fill = as.factor(cluster)), alpha = 0.4, linetype = 0) + theme_minimal() + labs(title = "Geopolitical Decisions Based on AI Capabilities", x = "Principal Component 1 (AI Capability)", y = "Principal Component 2 (Government Strategy)", color = "Cluster", shape = "Cluster") + scale_fill_manual(values = c("#FF9999", "#99CCFF", "#66CC66"), labels = c("AI Leaders", "Emerging AI Players", "Tech Adopters")) + theme(text = element_text(size = 16))

# data prepare for clustering visualization princ <- prcomp(scaled_data) nComp <- 2 project2D <- as.data.frame(predict(princ, newdata=scaled_data)[,1:nComp]) hclust.project2D <- cbind(project2D, cluster=as.factor(groups), country=df$Country) head(hclust.project2D) library('grDevices') find_convex_hull <-function(proj2Ddf,groups) { do.call(rbind, lapply(unique(groups), FUN= function(c){ f<-subset(proj2Ddf,cluster==c); f[chull(f),] } ) ) } hclust.hull <-find_convex_hull(hclust.project2D,groups) library(ggplot2) ggplot(hclust.project2D, aes(x=PC1,y=PC2)) + geom_point(aes(shape=cluster,color=cluster)) + geom_text(aes(label=country,color=cluster),hjust=0,vjust=1, size=3) + geom_polygon(data=hclust.hull, aes(group=cluster,fill=as.factor(cluster)), alpha=0.4, linetype=0) + theme(text=element_text(size=20))