XAI meets Natural Language Processing (PyConDE Berlin 2022)
As people tend to be more aware of AI systems and their impact, AI ethics and transparency become more and more relevant. Explainable AI (XAI) is a not-so-new term to collect methods and techniques to make predictions of AI systems more understandable. Which data points build the basis for model fitting? How is the model trained, based on which premises and assumptions? Which decisions, which parameters lead to the optimized outcome? And, most important, which model weights and decision paths result in which predictions?
