Transparency and Explainability

Machine learning models, particularly deep learning models, can often be complex and difficult to interpret, leading to concerns about their transparency and explainability. Ensuring that your model's decision-making process can be understood by stakeholders is essential for building trust and confidence in the technology. Techniques such as LIME, SHAP, and attention mechanisms can help improve the interpretability of machine learning models.