Causality-Driven Neural Network Repair: Challenges and Opportunities
This work addresses the challenge of debugging and repairing neural networks for improved reliability in AI applications, but it is incremental as it builds on existing causal methods without presenting new empirical results.
The paper tackles the problem of deep neural networks relying on statistical correlations rather than causal reasoning, which limits robustness and interpretability, and explores causal inference techniques like causal debugging and counterfactual analysis to identify and correct failures, aiming to enhance reliability in areas such as fairness and adversarial robustness.
Deep Neural Networks (DNNs) often rely on statistical correlations rather than causal reasoning, limiting their robustness and interpretability. While testing methods can identify failures, effective debugging and repair remain challenging. This paper explores causal inference as an approach primarily for DNN repair, leveraging causal debugging, counterfactual analysis, and structural causal models (SCMs) to identify and correct failures. We discuss in what ways these techniques support fairness, adversarial robustness, and backdoor mitigation by providing targeted interventions. Finally, we discuss key challenges, including scalability, generalization, and computational efficiency, and outline future directions for integrating causality-driven interventions to enhance DNN reliability.