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A look into IBM’s AI ethics governance framework

IBM Journey to AI blog

IBM watsonx.governance ™, a component of the watsonx™ platform that will be available on December 5 th , helps organizations monitor and govern the entire AI lifecycle. It helps accelerate responsible, transparent and explainable AI workflows.

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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. Monitor, catalog and govern models from anywhere across your AI’s lifecycle.

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Bring light to the black box

IBM Journey to AI blog

The solution: IBM watsonx.governance Coming soon, watsonx.governance is an overarching framework that uses a set of automated processes, methodologies and tools to help manage an organization’s AI use. Built on three critical principles, watsonx.governance helps meet the needs of your organization at any step in the AI journey: 1.

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How to responsibly scale business-ready generative AI

IBM Journey to AI blog

You can start by learning more about the advances IBM is making in new generative AI models with watsonx.ai and proactively put watsonx.governance in place to drive responsible, transparent and explainable AI workflows, today and for the future. What is watsonx.governance?

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How data stores and governance impact your AI initiatives

IBM Journey to AI blog

Among the tasks necessary for internal and external compliance is the ability to report on the metadata of an AI model. Metadata includes details specific to an AI model such as: The AI model’s creation (when it was created, who created it, etc.)

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Preparing for the EU AI Act: Getting governance right

IBM Journey to AI blog

In addition, stakeholders from corporate boards to consumers are prioritizing trust, transparency, fairness and accountability when it comes to AI. Risk management – preset risk thresholds, and proactively detect and mitigate AI model risks. Monitor for fairness, drift, bias and new generative AI metrics.

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3 key reasons why your organization needs Responsible AI

IBM Journey to AI blog

Documented, explainable model facts are necessary when defending analytic decisions. An AI Governance solution driving responsible, transparent and explainable AI workflows The right AI governance solution can help to better direct, manage and monitory your organization’s AI activities.