Causality in Neural Networks -- An Extended Abstract
This work addresses the need for more reliable and interpretable AI systems for real-world deployment, though it appears incremental as it builds on existing causal ideas in machine learning.
The paper tackles the problem of integrating causal reasoning into deep learning models to improve their trustworthiness and explainability, aiming to achieve better performance in fairness and disentangled representation.
Causal reasoning is the main learning and explanation tool used by humans. AI systems should possess causal reasoning capabilities to be deployed in the real world with trust and reliability. Introducing the ideas of causality to machine learning helps in providing better learning and explainable models. Explainability, causal disentanglement are some important aspects of any machine learning model. Causal explanations are required to believe in a model's decision and causal disentanglement learning is important for transfer learning applications. We exploit the ideas of causality to be used in deep learning models to achieve better and causally explainable models that are useful in fairness, disentangled representation, etc.