Probabilistic verification of fairness properties via concentration
Abstract
As machine learning systems are increasingly used to make real world legal and financial decisions, it is of paramount importance that we develop algorithms to verify that these systems do not discriminate against minorities. We design a scalable algorithm for verifying fairness specifications. Our algorithm obtains strong correctness guarantees based on adaptive concentration inequalities; such inequalities enable our algorithm to adaptively take samples until it has enough data to make a decision. We implement our algorithm in a tool called VeriFair, and show that it scales to large machine learning models, including a deep recurrent neural network that is more than five orders of magnitude larger than the largest previously-verified neural network. While our technique only gives probabilistic guarantees due to the use of random samples, we show that we can choose the probability of error to be extremely small.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 10, 2019
- Source ID
- 10.1145/3360544
Entities
People
- Armando Solar-lezama
- Osbert Bastani
- Xin Zhang
Organizations
- Massachusetts Institute of Technology
- Office of Naval Research
- University of Pennsylvania