Remove Explainability Remove Metadata Remove Responsible AI
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3 key reasons why your organization needs Responsible AI

IBM Journey to AI blog

Gartner predicts that the market for artificial intelligence (AI) software will reach almost $134.8 Achieving Responsible AI As building and scaling AI models for your organization becomes more business critical, achieving Responsible AI (RAI) should be considered a highly relevant topic. billion by 2025.

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Google AI Introduces Croissant: A Metadata Format for Machine Learning-Ready Datasets

Marktechpost

Database metadata can be expressed in various formats, including schema.org and DCAT. ML data has unique requirements, like combining and extracting data from structured and unstructured sources, having metadata allowing for responsible data use, or describing ML usage characteristics like training, test, and validation sets.

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Bring light to the black box

IBM Journey to AI blog

Success in delivering scalable enterprise AI necessitates the use of tools and processes that are specifically made for building, deploying, monitoring and retraining AI models. Responsible AI use is critical, especially as more and more organizations share concerns about potential damage to their brand when implementing AI.

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How data stores and governance impact your AI initiatives

IBM Journey to AI blog

But the implementation of AI is only one piece of the puzzle. The tasks behind efficient, responsible AI lifecycle management The continuous application of AI and the ability to benefit from its ongoing use require the persistent management of a dynamic and intricate AI lifecycle—and doing so efficiently and responsibly.

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Advancing AI trust with new responsible AI tools, capabilities, and resources

AWS Machine Learning Blog

As generative AI continues to drive innovation across industries and our daily lives, the need for responsible AI has become increasingly important. At AWS, we believe the long-term success of AI depends on the ability to inspire trust among users, customers, and society.

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How to use foundation models and trusted governance to manage AI workflow risk

IBM Journey to AI blog

AI governance refers to the practice of directing, managing and monitoring an organization’s AI activities. It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. ” Are foundation models trustworthy? . ” Are foundation models trustworthy?

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How IBM and the Data & Trust Alliance are fostering greater transparency across the data ecosystem

IBM Journey to AI blog

Strong data governance is foundational to robust artificial intelligence (AI) governance. Companies developing or deploying responsible AI must start with strong data governance to prepare for current or upcoming regulations and to create AI that is explainable, transparent and fair.

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