no code implementations • 26 Oct 2020 • Amir Sepehri, Cyrus DiCiccio
For companies developing products or algorithms, it is important to understand the potential effects not only globally, but also on sub-populations of users.
no code implementations • 23 Jun 2020 • Kinjal Basu, Cyrus DiCiccio, Heloise Logan, Noureddine El Karoui
Incorporating fairness while building such systems is crucial and can have a deep social and economic impact (applications include job recommendations, recruiters searching for candidates, etc.).
no code implementations • 19 Jun 2020 • Preetam Nandy, Cyrus DiCiccio, Divya Venugopalan, Heloise Logan, Kinjal Basu, Noureddine El Karoui
Building fair recommender systems is a challenging and crucial area of study due to its immense impact on society.
1 code implementation • 29 Jan 2019 • Ye Tu, Kinjal Basu, Cyrus DiCiccio, Romil Bansal, Preetam Nandy, Padmini Jaikumar, Shaunak Chatterjee
In this work, we develop a framework for personalization through (i) estimation of heterogeneous treatment effect at either a cohort or member-level, followed by (ii) selection of optimal treatment variants for cohorts (or members) obtained through (deterministic or stochastic) constrained optimization.