What if data itself could reduce poverty?
Our new paper, “Data for Inclusion: The Redistributive Power of Data Economics”, shows how access to positive credit information can lower interest costs and inequality — even without income growth. #Econsky
🔗 arxiv.org/abs/2510.16009
What if data itself could reduce poverty?
Our new paper, “Data for Inclusion: The Redistributive Power of Data Economics”, shows how access to positive credit information can lower interest costs and inequality — even without income growth. #Econsky
🔗 arxiv.org/abs/2510.16009
New paper published in Artificial Intelligence and Law (Springer).
This study bridges causal inference and graph deep learning to mitigate bias in financial AI.
doi.org/10.1007/s105...
New paper published in Artificial Intelligence and Law (Springer).
This study bridges causal inference and graph deep learning to mitigate bias in financial AI.
doi.org/10.1007/s105...
Global trade is fragmenting. My new paper in Applied Economics Letters introduces an AI-based framework that uses Graph Neural Networks (GNNs) and Generative Adversarial Networks (GANs) to predict where diversification and resilience truly emerge.
🔗 doi.org/10.1080/1350... #EconSky
Global trade is fragmenting. My new paper in Applied Economics Letters introduces an AI-based framework that uses Graph Neural Networks (GNNs) and Generative Adversarial Networks (GANs) to predict where diversification and resilience truly emerge.
🔗 doi.org/10.1080/1350... #EconSky
C Giray Aksoy, N Bloom, S Davis, V Marino, C Özgüzel
cepr.org/voxeu/column...
#EconSky
C Giray Aksoy, N Bloom, S Davis, V Marino, C Özgüzel
cepr.org/voxeu/column...
#EconSky
elpais.com/ciencia/2025...
elpais.com/ciencia/2025...
New paper out: Causal GNNs and the Anthropology of Data.
What if financial AI could distinguish between real causes and historical bias?
We introduce Causal Graph Neural Networks to embed fairness at the model design level
📄 @ssrn.bsky.social: papers.ssrn.com/sol3/papers....
#EconSky
New paper out: Causal GNNs and the Anthropology of Data.
What if financial AI could distinguish between real causes and historical bias?
We introduce Causal Graph Neural Networks to embed fairness at the model design level
📄 @ssrn.bsky.social: papers.ssrn.com/sol3/papers....
#EconSky
elpais.com/economia/202...
elpais.com/economia/202...
elpais.com/economia/neg...
elpais.com/economia/neg...
cincodias.elpais.com/mercados-fin...
cincodias.elpais.com/mercados-fin...
elpais.com/economia/neg...
elpais.com/economia/neg...
www.project-syndicate.org/commentary/a...
www.project-syndicate.org/commentary/a...
cincodias.elpais.com/mercados-fin...
cincodias.elpais.com/mercados-fin...
elpais.com/ideas/2025-0...
elpais.com/ideas/2025-0...