Fairness and Algorithmic Discrimination

Par

Golnoosh Farnadi

Mila / Université de Montréal

Résumé:

AI and machine learning tools are being used with increasing frequency for decision making in domains that affect peoples' lives such as employment, education, policing and loan approval. These uses raise concerns about biases and algorithmic discrimination and have motivated the development of fairness-aware mechanisms in the machine learning (ML) community and the operations research (OR) community, independently. In this talk, I will show how to ensure that the inference and predictions produced by a learned model are fair. Moreover, I will presents methods to ensure fairness in solutions of an optimization problem. I will conclude my talk with my research agenda to build on the complementary strengths of fairness methods in ML and OR and integrate ideas from them into a single system to build trustworthy AI.

Biographie :

Golnoosh obtained her PhD from KU Leuven and UGent in 2017. During her PhD, she addressed several problems in user modeling by applying and developing statistical machine learning algorithms. She later joined the LINQS group of Lise Getoor at UC Santa Cruz, to continue her work on learning and inference in relational domains. She is currently a post-doctoral IVADO fellow at UdeM and Mila, working with professor Simon Lacoste-Julien and professor Michel Gendreau on fairness-aware AI. She has been a visiting scholar at multiple institutes such as UCLA, University of Washington, Tacoma, Tsinghua University, and Microsoft Research, Redmond. She has had successful collaborations that are reflected in her several publications in international conferences and journals. She has also received two paper awards for her work on statistical relational learning frameworks. She has been an invited speaker and a lecturer in multiple venues and the scientific director of IVADO/Mila "Bias and Discrimination in AI" online course.