Over to you, Asghar.

Thank you.

Hi everyone, my name is Oskar.

I'm a research fellow at the foresight and policy modeling unit at IFPRI and today I'm going to tell you about most recent advancements in popularizing the use of our workhorse model called RIAPA, which is acronym for Rural Investment and Policy Analysis.

So this model is widely known in narrow circles and we maintain one of the largest single country repositories in the world.

We cover 42 countries and In two words, this is a relatively complicated economic model that captures the entire economy, general equilibrium effects across producers, households, and all different sorts of economic agents.

And given our focus here at IFPRI, we give a particular emphasis and focus on the primary agriculture and agro-processing sectors, which are presented in better details compared to other sectors like manufacturing or services, but these sectors are also there.

When AI becomes the interface to a policy model such as RIAPA, How do you ensure that users can actually trace an AI generated response back to the underlying data model assumptions and uncertainty?