Ensuring algorithmic decisions are transparent and understandable involves implementing explainability techniques and frameworks that make AI systems' operations clear to users. This can be achieved through methods like model interpretability tools (e.g., SHAP, LIME) and documentation practices such as model cards, which provide insights into how decisions are made and the factors influencing them.

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