Nov 28, 2019 · AI explainability also helps an organization adopt a responsible approach to AI development. As AI becomes more advanced, humans are challenged to comprehend and retrace …

Dec 6, 2022 · Explainability example Per GDPR (General Data Protection Regulation), a guest must explicitly opt in to use the hotel room assistant. Additionally, they will be provided with a transparent …

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（XAI）とは、機械学習アルゴリズムによって生成された結果や判断の根拠を、人間が理解し、信頼できるようにするための一連のプロセスや方法のことです。

설명 가능한 AI(XAI)를 통해 사용자는 머신 러닝 알고리즘이 생성한 결과와 출력을 이해하고, 신뢰할 수 있습니다.

AI interpretability is the ability to understand and explain the decision-making processes that power artificial intelligence models.

Anhand von erklärbarer künstlicher Intelligenz (Explainable Artificial Intelligence, XAI) können Nutzer die von Algorithmen des maschinellen Lernens erzeugten Ergebnisse und Ausgaben verstehen und …

For IBM, trust is a foundational pillar of AI. Check out our full Innovation panel to learn more about trust, fairness and governance with AI.

IBM’s governed data and AI technology is structured on the application of our fundamental principles for ethical AI: transparency, explainability, fairness, robustness, and privacy. These five focus areas are …

Oct 30, 2023 · 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. …