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Explainable artificial intelligence (XAI) is a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact and potential biases. It helps characterize model accuracy, fairness, transparency and ...
什么是可解释 AI？. 可解释人工智能 (XAI) 是一组流程和方法，让人类用户可以理解并信任机器学习算法创建的结果和输出。. 可解释 AI 用于说明 AI 模型、其预期影响和潜在偏见。. 它可帮助描述模型的准确性、公平性、透明度以及人工智能驱动型决策的结果 ...
Explainable AI. To trust AI systems, explanations can go a long way. We’re creating tools to help debug AI, where systems can explain what they’re doing. This includes training highly optimized, directly interpretable models, as well as explanations of black-box models and visualizations of neural network information flows.
¹ “Explainable AI”(ibm.com 외부 링크), The Royal Society, 2019년 11월 28일. ² ” Explainable Artificial Intelligence ”(ibm.com 외부 링크), Jaime Zornoza, 2020년 4월 15일. ³ ” Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI ”(ibm.com 외부 링크 ...
AI systems must be transparent and explainable. IBM believes that technology companies need to be clear about who trains their AI systems, what data was used in that training and, most importantly, what went into their algorithms’ recommendations. IBM has also developed five pillars to guide the responsible adoption of AI technologies. These ...
AutoPeptideML: A study on how to build more trustworthy peptide bioactivity predictors. Our trust in technology relies on understanding how it works. It’s important to understand why AI makes the decisions it does. We’re developing tools to make AI more explainable, fair, robust, private, and transparent.
Making AI more trusted, by making it explainable Further, AI explainability is increasingly important among business leaders and policymakers. In fact, 68 percent of business leaders believe that customers will demand more explainability from AI in the next three years, according to an IBM Institute for Business Value survey.
AI Ethics. AI ethics. IBM is helping to advance responsible AI with a multidisciplinary, multidimensional approach. Learn about foundation model ethics. Now is the moment for responsible AI. Businesses are facing an increasingly complex, ever-changing global regulatory landscape when it comes to AI. The IBM approach to AI ethics balances ...
説明可能なAIとは. 説明可能な人工知能（XAI）は一組のプロセスと方法で、機械学習アルゴリズムで生成した結果とアウトプットを、人間のユーザーが理解し、信用できるようにします。. 説明可能な AI は、AIモデルとその影響および潜在的なバイアスを説明 ...
AI must be trustworthy, and for stakeholders to trust AI, it must be transparent. Technology companies must be clear about who trains their AI systems, what data was used in that training, and, most importantly, what went into their algorithm’s recommendations. If we are to use AI to help make important decisions, it must be explainable.