Counterfactual Representation

Counterfactual representation research focuses on generating alternative versions of data—like text or images—that differ minimally from the original but change a model's prediction or behavior. Current work explores methods for creating these counterfactuals by manipulating model representations directly, often leveraging techniques from causal inference and generative models like transformers and GANs, to understand model decision-making and mitigate biases. This approach has implications for improving model explainability, enhancing fairness in machine learning, and developing more robust and privacy-preserving systems. The ability to generate meaningful counterfactuals is proving valuable across various applications, including bias mitigation in classification and defending against membership inference attacks.