Sorry, we can’t find that page that you’re looking for. You can try again by going back to the homepage.
The use of standard datasets, models, and algorithms often incorporates and exacerbates social biases in systems that use machine learning and artificial intelligence. Context-aware design and implementation of automated decision making algorithms is, therefore, an important and necessary venture. Our group aims to mitigate social biases in AI, with a focus on providing feasible debiased alternatives to currently-used models.
People
Faculty - Elisa Celis, Nisheeth VishnoiPostdocs - Lingxiao Huang
PhD students - Vijay Keswani, Anay Mehrotra
Undergraduate students - Chris Hays, Yi Chern Tan
Alumni - Sayash Kapoor, Farnood Salehi, Damian Straszak, Julia Wei
Demos
Multiwinner Voting
Elect a committee that is balanced across different attribute types. Deployed in Swiss elections. [Video]
Ranking
Prototype for gender-balanced rankings with applications to search engines, newsfeeds, and recommendation systems.
Classification
Python notebook for a meta fair classification algorithm, works for various fairness metrics. Deployed in IBM AIF 360.
Debiasing Data
Python notebook for learning and evaluating unbiased maximum-entropy distributions from biased datasets.