How Can AI Models Be Made More Transparent and Explainable?
To enhance transparency and explainability, several strategies can be employed:
Data Transparency: Providing access to the datasets used in training AI models allows others to evaluate the quality and relevance of the data. Model Documentation: Comprehensive documentation of the AI models, including their algorithms, assumptions, and limitations, offers insights into how the models work. Interpretable Models: Using simpler, interpretable models where possible, such as decision trees, can make the decision-making process more understandable. Post-Hoc Explanations: Techniques like SHAP (Shapley Additive Explanations) or LIME (Local Interpretable Model-agnostic Explanations) can be used to interpret complex models by approximating them with simpler ones.
