Explaining text classifiers with counterfactual representations

This repository contains the code, data, and supplementary material for the experiments included in the paper:

Lemberger, P., & Saillenfest, A. (2024). Explaining Text Classifiers with Counterfactual Representations. In Proceedings of ECAI 2024 - 27th European Conference on Artificial Intelligence, pp. 890-897.

Environment

Create and start a new virtual environment:

conda create -n CFR python=3.10.9 anaconda
conda activate CFR

Data

Download and pre-process the data before running the experiments.

• EEEC+: data and pre-processing in "./datasets/EEEC/EEEC_3race"
• BiasInBios: data and pre-processing in "./datasets/biasbios"
• CEBaB:
  • data: https://cebabing.github.io/CEBaB/
  • pre-processing in "./datasets/CEBaB-v1.1"
• GloVe :
  • data:
    • https://nlp.biu.ac.il/~ravfogs/rlace/glove/glove-gender-data.pickle (GloVe embeddings with gender-bias labels)
    • https://nlp.biu.ac.il/~ravfogs/rlace/glove/glove-top-50k.pickle (150k GloVe embeddings)

Experiments on synthetic data (sections 5.1 and 5.2)

Run the notebooks:

• CFRs_EEECp_gender_balanced.ipynb
• CFRs_EEECp_gender_aggressive.ipynb
• CFRs_EEECp_race_balanced.ipynb
• CFRs_EEECp_race_aggressive.ipynb

Experiments on the natural dataset BiasInBios (sections 5.4 and Supplementary material D)

Run the notebook:

• CFRs_biasbios.ipynb

Experiments on CEBaB (section 5.3)

Run:

• CFRs_CEBaB_compare_methods.ipynb

Experiment on GloVe embeddings (supplementary material C)

Run the notebook:

• CFRs_GloVe.ipynb