Remove Data Quality Remove Explainability Remove Responsible AI
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EU AI Act: What businesses need to know as regulations go live

AI News

They must demonstrate tangible ROI from AI investments while navigating challenges around data quality and regulatory uncertainty. Its already the perfect storm, with 89% of large businesses in the EU reporting conflicting expectations for their generative AI initiatives. Whats prohibited under the EU AI Act?

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Considerations for addressing the core dimensions of responsible AI for Amazon Bedrock applications

AWS Machine Learning Blog

The rapid advancement of generative AI promises transformative innovation, yet it also presents significant challenges. Concerns about legal implications, accuracy of AI-generated outputs, data privacy, and broader societal impacts have underscored the importance of responsible AI development.

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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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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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AWS achieves ISO/IEC 42001:2023 Artificial Intelligence Management System accredited certification

AWS Machine Learning Blog

ISO/IEC 42001 is an international management system standard that outlines requirements and controls for organizations to promote the responsible development and use of AI systems. Responsible AI is a long-standing commitment at AWS. At Snowflake, delivering AI capabilities to our customers is a top priority.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

This includes features for model explainability, fairness assessment, privacy preservation, and compliance tracking. Your data team can manage large-scale, structured, and unstructured data with high performance and durability. Data monitoring tools help monitor the quality of the data.

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Pascal Bornet, Author of IRREPLACEABLE & Intelligent Automation – Interview Series

Unite.AI

In the context of AI specifically, companies should be transparent about where and how AI is being used, and what impact it may have on customers' experiences or decisions. Thirdly, companies need to establish strong data governance frameworks. In the context of AI, data governance also extends to model governance.