Fairness-Aware learning over Graphs
DATE/TIME: Tuesday, April 2nd, 12:00pm
SPEAKER: Oyku Deniz Kose
While graph-based ML models nicely integrate the nodal data with the connectivity, they also inherit potential unfairness. Using such ML models may therefore result in inevitable unfair results in various decision- and policy-making in the related applications. While fairness and explainability have attracted increasing attention in responsible ML, they are mostly under-explored in the graph domain.