Amy Hodler

This 90 minute workshop explores how to leverage the open-source ecosystem and cutting-edge technologies in causal machine learning to enhance decision-making processes.

In a world obsessed with making predictions and generative AI, we often overlook the crucial task of making sense of these predictions and understanding results.

We all know everything is connected, but what can you do with this information? Some organizations use the relationships between data to improve predictions, but there's a huge untapped opportunity to apply these patterns to shape more positive outcomes.

The world is full of poorly tuned queries and badly designed data models that don’t accurately reflect the business. This hairball leads to excessive hardware and operational costs – not to mention a frustrating degree of customization required for each and every application.

Creating apps to model complex and dynamic environments is challenging and often implies a longtail maintenance burden. In this session, we’ll look at how a relational approach to graphs can tackle one of the most complex situations to model: disasters in cities around the world.The UN Office for Information Technology has partnered with Relational.AI to support the analysis of ongoing stresses an

Graph enhanced AI and ML are changing the landscape of intelligent applications. In this workshop, we’ll focus on using graph feature engineering to improve the accuracy, precision, and recall of machine learning models.

As creators and users of artificial intelligence (AI), we have a duty to guide the development and application of AI in ways that fit our social values, in particular, to increase accountability, fairness and public trust.

Data scientists use graph algorithms for community detection because relationships are one of the most predictive indicators of behavior and preferences. Community detection algorithms help us infer similar preferences in peer groups, anticipate future behavior, estimate group resiliency, find hierarchies, and prepare data for other analysis.