Building and scaling AI with trust and transparency is crucial for any organization. For explainable AI to be effective, it must enable transparency, explain the predictions and algorithm and contextualize the explanations effectively.
For example, imagine a medical center that uses AI-driven tools to assist oncologists in diagnosing various types of cancer, where doctors are the primary users of the AI system. Analysis of extensive patient data is considered to pinpoint influential factors such as genetic markers and imaging characteristics, leading to diagnosis recommendations.
To mitigate these risks, robust training, guidelines, audits and regulatory collaboration are essential, along with ongoing monitoring to identify and address misinterpretations promptly.Several techniques developed for XAI provide interpretations of the models' decisions such as SHAP, LIME, partial dependence plot and individual condition expectations plots. We categorize them into three types.
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