Consistent Counterfactuals

Consistent counterfactuals are synthetic data points minimally altered from existing data to change a model's prediction, offering insights into model behavior and improving fairness and robustness. Research focuses on generating these counterfactuals across various data types (text, graphs, images) using diverse methods, including generative adversarial networks (GANs), diffusion models, and transformer networks, often incorporating constraints to ensure realism and interpretability. This work is significant for enhancing model explainability, mitigating bias, and improving the reliability of machine learning predictions across domains like healthcare and automated essay scoring.