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Explainability goes hand in hand with ethical AI. It's not enough for your AI to be accurate; it also needs to be understandable. Stakeholders need to know *why* the AI made a particular decision. IBM advocates for using explainable AI (XAI) techniques to make AI models more transparent and interpretable. This can involve using simpler models, visualizing decision-making processes, or providing explanations for individual predictions. By making AI more explainable, you can build trust with users, identify potential biases, and improve the overall quality of your AI solutions.
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