What is explainable AI? - IBM Explainable AI is one of the key requirements for implementing responsible AI, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability ³ To help adopt AI responsibly, organizations need to embed ethical principles into AI applications and processes by building
Explainability example - IBM Explainability is needed to build public confidence in disruptive technology, to promote safer practices, and to facilitate broader societal adoption There are situations where users may not have access to the full decision process that an AI might go through, e g financial investment algorithms
How IBM makes AI based on trust, fairness and explainability AI Explainability Explainability in AI is multifaceted One approach does not fit all cases, because different processes require different explanations For example, a loan officer asks why you recommended rejection of a loan; the customer wants to know why their loan was denied; the regulator wants proof that your system isn’t discriminatory
What Is AI Interpretability? - IBM AI interpretability focuses on understanding the inner workings of an AI model while AI explainability aims to provide reasons for the model's outputs Interpretability is about transparency, allowing users to comprehend the model's architecture, the features it uses and how it combines them to deliver predictions
Was ist erklärbare KI (XAI)? | IBM Anhand von erklärbarer künstlicher Intelligenz (Explainable Artificial Intelligence, XAI) können Nutzer die von Algorithmen des maschinellen Lernens erzeugten Ergebnisse und Ausgaben verstehen und ihnen vertrauen
What is responsible AI? | IBM Explainability Machine learning models such as deep neural networks are achieving impressive accuracy on various tasks But explainability and interpretability are ever more essential for the development of trustworthy AI
What Is AI Transparency? - IBM AI explainability, or explainable AI (XAI), is a set of processes and methods that allow human users to comprehend and trust the results and output created by machine learning models Model explainability looks at how an AI system arrives at a specific result and helps to characterize model transparency
Che cosè lAI spiegabile? - IBM La cosiddetta AI spiegabile (o eXplainable AI , XAI) consente agli utenti umani di comprendere e ritenere affidabili i risultati e gli output generati mediante algoritmi di machine learning
説明可能なAIとは - IBM 説明可能な人工知能（xai）は、機械学習アルゴリズムによって生成された結果とアウトプットを、人間のユーザーが理解し信頼できるようにする一連のプロセスや方法です。