A Causal Argumentation Method for Explainability of Machine Learning Models

arXiv:2604.15898v1 Announce Type: new Abstract: For around a decade, non-symbolic methods have been the option of choice when explaining complex machine learning (ML) models.

arXiv:2608.12555v1 Announce Type: new Abstract: Predictive explanation methods attribute a model output; they do not, by themselves, attribute an intervention effect on the

arXiv:2609.19944v1 Announce Type: new Abstract: Large language models (LLMs) have been applied to causal discovery, but candidate-graph generation rarely treats premature omission

arXiv:2605.11365v1 Announce Type: new Abstract: Automated systems built on artificial intelligence (AI) are increasingly deployed across high-stakes domains, raising critical

arXiv:2605.11687v1 Announce Type: new Abstract: Financial institutions increasingly require AI explanations that are persistent, cross-validated across methods, and

arXiv:2604.27007v1 Announce Type: new Abstract: We provide a causal analysis of Binary Spiking Neural Networks (BSNNs) to explain their behavior. We formally define a BSNN and

arXiv:2605.11595v1 Announce Type: new Abstract: The EU Artificial Intelligence Act (Regulation 2024/1689), fully applicable to high-risk systems from August 2026, creates urgent

arXiv:2604.19788v1 Announce Type: new Abstract: As Artificial Intelligence (AI) systems continue to grow in size and complexity, so does the difficulty of the quest for AI