From the course: Responsible AI: Principles and Practical Applications
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Responsible AI principles and practices
From the course: Responsible AI: Principles and Practical Applications
Responsible AI principles and practices
- In this video, you'll be introduced to emerging governance and technology strategies to support responsible and trustworthy AI development and deployment guided by responsible AI principles. Let's explore three strategies: AI documentation, fairness evaluation tools, and responsible AI training toolkits. First, the private and public sectors have promoted documentation of AI development. For example, IBM has developed FactSheets that encourage AI developers to record the purpose, performance, safety, security, and provenance of AI models. Google's Model Cards provide a framework for recording the provenance, usage, and ethics-informed evaluation of AI models. And Microsoft's Datasheets for Datasets and Transparency Notes encourage documentation of data provenance, composition, collection processes, motivations, and recommended uses. In the public sector, the cities of Helsinki and Amsterdam have launched AI…
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