Exploring Algorithmic Fairness in Robust Graph Covering Problems

NeurIPS 2019 Aida RahmattalabiPhebe VayanosAnthony FulginitiEric RiceBryan WilderAmulya YadavMilind Tambe

Fueled by algorithmic advances, AI algorithms are increasingly being deployed in settings subject to unanticipated challenges with complex social effects. Motivated by real-world deployment of AI driven, social-network based suicide prevention and landslide risk management interventions, this paper focuses on a robust graph covering problem subject to group fairness constraints... (read more)

PDF Abstract

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper

🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet