Causality in Neural Networks - An Extended Abstract

Reddy, A.G (2021) Causality in Neural Networks - An Extended Abstract. In: Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 19 May 2021 through 21 May 2021, Virtual, Online.

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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.

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Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: AI systems, Causal explanations, Causal reasoning, Extended abstracts, Learning models, Machine learning models, Real-world
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: Mrs Haseena VKKM
Date Deposited: 23 Nov 2021 05:59
Last Modified: 23 Nov 2021 05:59
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