4 stories tagged AI Explainability
Researchers have developed a three-stage method that translates a trained deep reinforcement learning policy into an executable Prolog program, making AI decisions readable, runnable, and editable by humans.
Researchers introduced PACE, a neuro-symbolic AI framework that generates realistic and actionable counterfactual explanations. This helps users understand why a machine learning model made a certain decision and what changes could alter the outcome.
Researchers have developed a causal framework to explain the behavior of Binary Spiking Neural Networks (BSNNs) using logic-based methods. They demonstrated this approach by training a BSNN on the MNIST dataset and applying SAT and SMT solvers to derive explanations.
Researchers introduce COMPASS, a system that automates prompt engineering for generating human-understandable explanations of AI task planning. The tool addresses the critical need for reliable, stakeholder-specific explanations in complex software systems.