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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
Alumni - Chris Hays, Sayash Kapoor, Farnood Salehi, Damian Straszak, Yi Chern Tan, 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.