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Abstract

As the use of machine learning (ML) algorithms in network science increases, so do the problems related to explainability, transparency, fairness, privacy, and robustness, to name a few. In this talk, I will give a brief overview of the field and present recent work from my lab on the (in)stability and explainability of node embeddings, attacks on ML algorithms for graphs, and equality in complex networks.

Bio
Tina Eliassi-Rad is a professor of computer science at Northeastern University and an external faculty member at Santa Fe Institute. She works at the intersection of AI and Network Science and cares about the impact of science and technology on the disadvantaged members of society.

Watch at: www.youtube.com

• Talks