If you're running a financial services firm, and you want to promote digital transformation, you need to decide what exactly needs to be transformed. "In all honesty, what areas aren't there?" says Teddy Flo, general counsel at Zest AI. "The financial services industry is vastly behind other consumer-centric industries in a lot of ways." Any and all transformations should have one goal, and one goal only: delivering to the customer -- ultimately enriching their relationships with banks and nonbanks alike. "You can't change anything for the better if everyone doesn't have equitable access to capital," Flo says. "The way we make decisions on credit should be fair and inclusive and done in a way that takes into account a greater picture of a person."

We should be living in a golden age of science. For centuries, the scientific method was defined by two pillars--theory and experiment. Now, we live in the age of Artificial Intelligence, which adds a vital third pillar. Without advanced computation, according to leading scientific bodies, discoveries of the past decade, such as the detection of the Higgs boson, the discovery of new drugs like halicin, which can kill strains of bacteria resistant to all known antibiotics, or the observation of gravitational waves, "would have been impossible". But despite these advances, scientific innovation today is too often defined by new use cases for existing technologies or refining previous advancements, rather than the creation of entirely new fields of discovery.

We should be living in a golden age of science. For centuries, the scientific method was defined by two pillars--theory and experiment. Now, we live in the age of Artificial Intelligence, which adds a vital third pillar. Without advanced computation, according to leading scientific bodies, discoveries of the past decade, such as the detection of the Higgs boson, the discovery of new drugs like halicin, which can kill strains of bacteria resistant to all known antibiotics, or the observation of gravitational waves, "would have been impossible". But despite these advances, scientific innovation today is too often defined by new use cases for existing technologies or refining previous advancements, rather than the creation of entirely new fields of discovery.

Disparate access to resources by different subpopulations is a prevalent issue in societal and sociotechnical networks. For example, urban infrastructure networks may enable certain racial groups to more easily access resources such as high-quality schools, grocery stores, and polling places. Similarly, social networks within universities and organizations may enable certain groups to more easily access people with valuable information or influence. Here we introduce a new class of problems, Graph Augmentation for Equitable Access (GAEA), to enhance equity in networked systems by editing graph edges under budget constraints. We prove such problems are NP-hard, and cannot be approximated within a factor of $(1-\tfrac{1}{3e})$. We develop a principled, sample- and time- efficient Markov Reward Process (MRP)-based mechanism design framework for GAEA. Our algorithm outperforms baselines on a diverse set of synthetic graphs. We further demonstrate the method on real-world networks, by merging public census, school, and transportation datasets for the city of Chicago and applying our algorithm to find human-interpretable edits to the bus network that enhance equitable access to high-quality schools across racial groups. Further experiments on Facebook networks of universities yield sets of new social connections that would increase equitable access to certain attributed nodes across gender groups.