Counterfactual Generation Method

Counterfactual generation methods aim to minimally alter input data to change a model's prediction, providing insights into model behavior and decision-making processes. Current research focuses on developing robust and efficient algorithms for generating these counterfactuals across various data types, including text and tabular data, often leveraging large language models or data morphology techniques to improve quality and interpretability. This work is significant for enhancing the trustworthiness and explainability of AI systems, with applications ranging from improving educational tools to detecting and mitigating bias in natural language processing models.