Counterfactual Data

Counterfactual data, representing what *could have been* instead of what *was*, is increasingly used to improve the robustness and fairness of machine learning models. Current research focuses on generating high-quality counterfactual datasets for various tasks, including image classification, natural language processing, and reinforcement learning, often employing diffusion models, variational autoencoders, and large language models to create these synthetic datasets. This work aims to mitigate biases, improve model generalization, and enhance explainability by revealing spurious correlations and causal relationships within data, leading to more reliable and trustworthy AI systems across diverse applications.