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Found 5 papers, 3 papers with code
Argumentative Causal Discovery
Causal discovery amounts to unearthing causal relationships amongst features in data.
Contestable AI needs Computational Argumentation
AI has become pervasive in recent years, but state-of-the-art approaches predominantly neglect the need for AI systems to be contestable.
Online Handbook of Argumentation for AI: Volume 4
This volume contains revised versions of the papers selected for the fourth volume of the Online Handbook of Argumentation for AI (OHAAI).
Shapley-PC: Constraint-based Causal Structure Learning with Shapley Values
Causal Structure Learning (CSL), also referred to as causal discovery, amounts to extracting causal relations among variables in data.
Causal Discovery and Knowledge Injection for Contestable Neural Networks (with Appendices)
Neural networks have proven to be effective at solving machine learning tasks but it is unclear whether they learn any relevant causal relationships, while their black-box nature makes it difficult for modellers to understand and debug them.
