This paper provides a causal analysis of Binary Spiking Neural Networks (BSNNs) to explain their behavior, representing their spiking activity as a binary causal model. By leveraging logic-based methods like SAT and SMT solvers, it computes abductive explanations for network classifications and demonstrates that these explanations do not contain irrelevant features, unlike SHAP.
The `FairMind` software prototype automates causal fairness analysis at the dataset level, addressing the lack of fairness consideration in most AutoML frameworks. It utilizes the standard fairness model and LLMs to generate accurate reports on fairness based on counterfactual causal effects.
This research addresses the challenge of simpler AI agents inadvertently forming a collective agent with distinct goals, crucial for advanced AI safety. It proposes defining collective agency behaviorally, viewing a group as a unified agent when its joint actions appear rational and goal-directed, formalized through causal games and abstraction.