Attribution Model

Attribution models aim to explain the internal workings of complex models, particularly deep learning systems, by identifying which input features most influence their outputs. Current research focuses on improving the accuracy and efficiency of these models, addressing challenges like handling diverse input formats (e.g., text, images), mitigating biases, and ensuring robustness against adversarial attacks. This work is crucial for enhancing the transparency and trustworthiness of AI systems, enabling better debugging, fairer decision-making, and improved understanding of model behavior across various applications, including natural language processing and network fault diagnosis.