Counterfactual Editing

Counterfactual editing aims to modify data (images or text) to explore "what if" scenarios, altering specific features while maintaining overall realism and consistency. Current research focuses on developing methods that accurately reflect causal relationships between features, using techniques like augmented structural causal models and leveraging pre-trained models such as CLIP and large language models for generation and editing. This field is crucial for improving the interpretability and robustness of AI models, particularly in applications like generative AI and natural language processing, by enabling more nuanced evaluation and data augmentation strategies. Furthermore, it addresses concerns about faithfulness and bias in AI explanations.