Challenges and Techniques in Achieving Interpretability and Transparency in AI Models
Interpretability and transparency are critical aspects of building trustworthy and ethical artificial intelligence (AI) models. As AI systems become more sophisticated, there is a growing need to understand the decision-making processes of these models. This article explores the challenges associated with achieving interpretability and transparency in AI models and discusses various techniques and approaches used to address these challenges. By making AI models interpretable and transparent, we can enhance their trustworthiness, enable better understanding of their predictions, and ensure accountability in critical domains such as healthcare, finance, and criminal justice. Challenges in AI Model Interpretability Interpreting AI models presents several […]