As the use of enterprise Artificial Intelligence and Machine Learning continues to grow, so too does the importance of building it responsibly. A challenge for many is that talking about responsible AI can be easier than putting it into practice. If you’re interested in learning how to operationalize responsible AI in your organization, this course is for you. In this course, you will learn how Google Cloud does this today, together with best practices and lessons learned, to serve as a framework for you to build your own responsible AI approach.
Overview
Syllabus
- Introduction
- Course introduction
- Google and responsible AI
- An introduction to Google’s AI Principles
- The Business Case for Responsible AI
- The Economist Intelligence Unit report
- The business case for responsible innovation
- AI’s Technical Considerations and Ethical Concerns
- AI’s technical considerations and ethical concerns
- Concerns about artificial intelligence
- Creating AI Principles
- How Google’s AI principles were developed
- Ethical issue spotting
- The ethical aims of Google’s AI principles
- Operationalizing AI Principles: Setting Up and Running Reviews
- Google’s AI Governance
- Google Cloud’s review process
- Celebrity Recognition Case Study
- Operationalizing AI Principles: Issue Spotting and Lessons Learned
- Issue spotting process
- What we’ve learned from operationalizing AI Principles: Challenges
- What we’ve learned from operationalizing AI Principles: Best practices
- Continuing the Journey Towards Responsible AI
- Continuing the journey towards responsible AI
- Rescource Links
- Your Next Steps
- Course Badge
