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- 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
- 설명 가능한 AI(XAI)란 무엇인가요? - IBM
설명 가능한 ai(xai)를 통해 사용자는 머신 러닝 알고리즘이 생성한 결과와 출력을 이해하고, 신뢰할 수 있습니다
- 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
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