#causality2 articles

2 articles

AINeutralarXiv – CS AI · 7h ago7/10

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HCP-DCNet: A Hierarchical Causal Primitive Dynamic Composition Network for Self-Improving Causal Understanding

Researchers introduce HCP-DCNet, a new AI framework that combines physical dynamics with symbolic causal reasoning to enable AI systems to understand cause-and-effect relationships. The system uses hierarchical causal primitives and can self-improve through interventions, potentially addressing current limitations in AI's ability to handle distribution shifts and counterfactual reasoning.

AINeutralarXiv – CS AI · 7h ago6/10

🧠

Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models

Researchers propose integrating causal methods into machine learning systems to balance competing objectives like fairness, privacy, robustness, accuracy, and explainability. The paper argues that addressing these principles in isolation leads to conflicts and suboptimal solutions, while causal approaches can help navigate trade-offs in both trustworthy ML and foundation models.