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AI Risk Management Frameworks

This whitepaper provides you with the following information:

  • The various AI Risk Management Frameworks in use today globally and how they can safeguard your projects against unforeseen risks;
  • The lifecycle of AI models and systems, allowing for better allocation of resources towards continuous improvement and innovation;
  • How these Frameworks align with global regulatory standards such as the OECD guidelines and the EU’s AI Act. 

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Artificial intelligence (AI) poses both challenges and opportunities for businesses. Owing to the plethora of complexities and risks involved, the thorough understanding, implementation, and assessment of all AI capabilities being used becomes even more critical for businesses hoping to leverage such capabilities responsibly. 

Consequently, appropriately using and understanding various AI Risk Management Frameworks becomes even more paramount. Our guide provides businesses with the knowledge they need to select and apply the most appropriate frameworks for their specific needs. 

This guide aims to empower businesses to make informed decisions about AI deployment, ensuring that they can navigate the challenges and opportunities of AI in a safe and compliant manner.

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Frequently Asked Questions (FAQs) related to AI Risk Management Framework

Here are some other commonly asked questions you might have:

These AI Risk Management Frameworks have been designed for anyone involved, directly or indirectly, in the development, use, evaluation, design, deployment, or assessment of AI models and systems. The language is intentionally kept accessible enough for non-AI professionals while retaining the technical depth necessary for practitioners from various domains. 

Considering how dynamically AI technologies are changing and evolving, it is crucial for organizations deploying these technologies to have proportional governance measures in place to manage any of the risks associated with the use of such technologies. Deployed effectively, such a Framework can enable organizations to navigate most of their AI-related risks with ease.

Effective management of AI-related risks would rely tremendously on six critical contexts: data handling, model selection and training, deployment and infrastructure, contracts and insurance, legal and regulatory compliance, and organizational culture. By prioritizing the mitigation of any AI-related risks and issues arising within these domains, organizations can curate AI usage in a manner that promises effective results. 

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