Interpretability and explainability
Interpretability and explainability in AI systems, particularly in large language models (LLMs) and GenAI, are crucial for fostering trust, enabling effective oversight, and ensuring responsible deployment. As these systems become more complex and their decision-making processes more opaque, the need for methods to understand and explain their outputs grows increasingly important. Interpretability allows stakeholders to peek inside the “black box” of AI, while explainability focuses on communicating how decisions are made in a way that humans can understand.
The following points outline key strategies for enhancing interpretability and explainability in AI systems, with a focus on practical approaches and real-world examples. By implementing these practices, organizations can create more transparent AI systems, facilitating better decision-making, regulatory compliance, and user trust.
- Model cards: Model cards provide...
