Explainable AI needs formalization

1. 1Identify the limitations of current XAI methods in attributing importance to input features
2. 2Develop formal frameworks for explaining ML model decisions
3. 3Evaluate the reliability of XAI methods in answering questions about ML models and their training data
4. 4Apply formalized XAI to real-world ML applications to improve model interpretability and trustworthiness

ML researchers and engineers benefit from formalized XAI to improve model interpretability and trustworthiness, while data scientists and analysts can apply these methods to better understand model decisions

💡 Current XAI methods are limited in their ability to reliably answer questions about ML models and their decisions

💡 Explainable AI needs formalization to improve model interpretability #XAI #ML