High Quality Counterfactuals

High-quality counterfactual generation aims to create plausible and minimally-altered hypothetical scenarios that change a model's prediction, providing valuable insights into model behavior and decision-making processes. Current research focuses on improving the quality of generated counterfactuals across various data types (text, time-series, images) using diverse methods including causal models, diffusion models, genetic algorithms, and large language models, often incorporating constraints like sparsity and plausibility. This work is significant for enhancing explainability in AI, improving model fairness and robustness, and facilitating more informed decision-making in high-stakes applications such as healthcare and finance.