Network Fairness: A novel complex network approach for tackling inequalities in society and algorithms

Social inequalities are on the rise and will have devastating impacts on education, healthcare, economiesand societies for many generations to come.

Structural barriers to equality, despite being complex and dynamic,are poorly understood through the lens of complex evolving social networks.

More crucially, withthe rise of AI and machine-learning algorithms, it is extremely important to detect, forecast, and mitigatethose inequalities in a systematic manner in order to avoid unintentional algorithmic consequences.A network fairness framework is proposed, premised on topological and temporal features of social interactionsthat shape the formation and evolution of inequalities.

These features include (1) people havemultiple and correlated attributes that determine how they identify with groups and interact with others,(2) people belong to a variety of social groups with different sizes, hierarchies, and historical precedents,and (3) interactions between people evolve over time in a hybrid space of society and algorithms.To this end, I will develop a suite of dynamical complex network models of inequality that are driven bysocial theories (e.g., homophily, intersectionality, consolidation) and calibrated and evaluated with big dataand network experiments.

This will allow us, for the first time, to investigate inequalities that arise fromnetwork-based algorithms in a systematic manner.

More importantly, I will devise a novel methodology ofnetwork intervention, a set of data-driven principles for tackling network inequalities in a broad range ofapplications.

Finally, based on the models developed in this project, I will create a cutting-edge interactivesoftware – NetFair – to visualize and forecast the evolution of inequalities and implement various fairnesscriteria. The software will contribute towards bridging the gap between research and policy applicationsin academia and industry.