🎯 Principles for trustworthy AI systems [part 2/5]
Get started with AI | How-to guides and features
As more companies try to adopt AI in one way or another, business leaders grapple with virtuous decisions about its design and use.
Global AI regulations will eventually address challenging concerns, but they won't be uniform and won't provide the needed solutions.
So, until then, we will need to work to bridge the gaps. And there are many.
Over the upcoming weeks in the “How-to” section, we will discuss best practices for instilling integrity through governance across the AI lifecycle.
A 5 part series extracted from a workshop “Enterprise AI, a toolbox for risk management”1, to help you understand:
What are the principles of trustworthy AI (this part)
How to operationalize trustworthy AI Governance
How to instill AI integrity
Recommendations and best practices
An improper implementation, a socially insensitive data label, or negligent data management can easily lead to an auditing nightmare:
How can you adapt to new AI technologies with ease?
What are the best practices to safely utilize and govern AI?
How can you ensure that your AI project does not become a liability?