Motivated from the problems faced by under-served communities or in under-resourced settings, we are working to define and quantify fairness in machine learning. Our aim is to build intelligent decision-making systems that effectively trade-off fairness, efficiency, and transparency while being robust to uncertainty/noise in the data.

We are developing optimal, fair, and interpretable machine learning algorithms suitable for decision-making in socially sensitive settings. Our work is motivated by the increased use of machine learning techniques to make decisions affecting marginalized communities and vulnerable populations.

We are also focused on speeding-up computation of optimal decision-trees that can capture arbitrary constraints (e.g., fairnees, interpretability) to faciliate open-world deployment. Our approaches are 50x faster than existing techniques.

Strong optimal classification trees

(*) S. Aghaei, A. Gomez, P. Vayanos

Under review at Operations Research, January 2021.

Learning optimal and fair decision trees for non-discriminative decision-making

(*) S. Aghaei, (*) M.J. Azizi, P. Vayanos

In Proceedings of 33rd AAAI Conference on Artificial Intelligence, 2019.

note: acceptance rate ~16% in year of submission

Learning optimal classification trees: strong max-flow formulations

(*) S. Aghaei, A. Gomez, P. Vayanos

Technical report, available on Optimization Online, 2020.

Designing fair, efficient, and interpretable policies for prioritizing homeless youth for housing resources

(*) M. J. Azizi, P. Vayanos, B. Wilder, E. Rice and M. Tambe

In Proceedings of the 15th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR), 2018.

Invited to Constraints journal fast track for outstanding papers.