Test your knowledge on the foundations of Explainable AI (XAI) with this quiz! E...

Recap: Foundations of Explainable AI (XAI)

• There are 2 basic interaction types: select and explore. Other types include reconfigure, encode, abstract/elaborate, filter, and connect.
• There are 3 view arrangement tabs: dashboard, scrolling, and direct vs. indirect manipulation.
• Explainability refers to revealing the decision-making processes of AI models in a faithful and comprehensible manner.
• XAI techniques include interpretable models, adversarial testing and counterfactuals, attribution methods, activation and feature visualizations, and XAI verification through occlusion and perturbation.
• XAI is necessary for monitoring and explaining AI-based decision-making due to legal requirements and concerns about fairness, causality, reliability, trust, and transparency.
• XAI can be done a priori (before training and testing), ad-hoc (during training and testing), or post-hoc (after training and testing).
• Algorithm-centric XAI focuses on explaining how the model decided, while human-centric XAI aligns the model's decision with the user's understanding.
• XAI as a process involves input data, an AI model, an explainer, external resources, and transition functions.
• XAI pipeline involves input data, an AI model, an explainer, external resources, transition functions, and explanations (visualizations, verbalizations, surrogate models, etc.).
• XAI pipeline can suggest changes in the ML model, parameters, and data for the final model.
• XAI pipeline aims to achieve intelligibility, transparency, interpretability, and comprehensibility.
• XAI pipeline can answer questions such as "What is my model learning?" and "Is the model biased?" at different stages of training, testing, and deployment.

Test your knowledge on the foundations of Explainable AI (XAI) with this quiz! Explore the different types of interactions and view arrangements, as well as XAI techniques such as interpretable models, attribution methods, and more. Understand why XAI is necessary for monitoring and explaining AI-based decision-making, and learn about the XAI pipeline and its aim for intelligibility, transparency, and comprehensibility. Put your understanding to the test and answer questions such as "What is my model learning?"