Model fairness is becoming important in class-incremental learning for Trustworthy AI. While accuracy has been a central focus in class-incremental learning, fairness has been relatively understudied. However, naively using all the samples of the current task for training results in unfair catastrophic forgetting for certain sensitive groups including classes. We theoretically analyze that forgetting occurs if the average gradient vector of the current task data is in an "opposite direction" compared to the average gradient vector of a sensitive group, which means their inner products are negative. We then propose a fair class-incremental learning framework that adjusts the training weights of current task samples to change the direction of the average gradient vector and thus reduce the forgetting of underperforming groups and achieve fairness. For various group fairness measures, we formulate optimization problems to minimize the overall losses of sensitive groups while minimizing the disparities among them. We also show the problems can be solved with linear programming and propose an efficient Fairness-aware Sample Weighting (FSW) algorithm. Experiments show that FSW achieves better accuracy-fairness tradeoff results than state-of-the-art approaches on real datasets.
$ conda env create -f py39.yaml
$ pip install cl-gym
For more information on the cl-gym library, refer to imirzadeh/CL-Gym.
- MNIST experiment
$ bash scripts/script_MNIST.sh
- FashionMNIST experiment
$ bash scripts/script_FashionMNIST.sh
- BiasedMNIST experiment
$ bash scripts/script_BiasedMNIST.sh
- Drug experiment
$ bash scripts/script_Drug.sh
Both MOSEK and CPLEX optimization solvers are free for students and academics. Installing these solvers is straightforward, as you can simply follow the provided guidelines for each package.
# MOSEK
https://www.mosek.com/products/academic-licenses/
https://www.mosek.com/downloads/
# CPLEX
https://community.ibm.com/community/user/datascience/blogs/xavier-nodet1/2020/07/09/cplex-free-for-students