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Building a Responsible AI Program: Context, Culture, Content, and Commitment

Building a Responsible AI Program: Context, Culture, Content, and Commitment

1h 30mBeginner2024-04-10

Authors

Katrina Ingram

Katrina Ingram

Course details

Organizations who use artificial intelligence need to ensure they do so in a socially responsible way, and in this course, Katrina Ingram, the Founder and CEO of Ethically Aligned AI, shows you how to implement a framework to operationalize responsible AI. Katrina shows you how to implement a 4Cs framework—context, content, culture, and commitment—as the basis for a responsible AI program, and provides practical examples of the framework in action. She also explains how to connect AI ethics to organizational values, who should be involved in a Responsible AI program, how to assess and augment governance structures to include oversight for AI, how to think about and use data responsibly, and more. Plus, learn about a process and structure to handle ethical deliberations and how to apply ethics tools to support a responsible AI program.

Skills covered

Project LeadershipResponsible AIProject ManagementArtificial Intelligence (AI)One-Off

Concepts

0. Introduction

  • 01 - Actionable steps to responsible AI
  • 02 - Moving from principles to practice
  • 03 - Introducing the Landon Hotel

1. Context - Law, Ethics, and AI Risk

  • 04 - Connector - Start with context
  • 05 - The AI legal landscape
  • 06 - Understanding ethical AI risks
  • 07 - Getting clear on organizational values and AI risks
  • 08 - The AI ethics statement, policies, and metrics

2. Context - AI in Your Organization

  • 09 - Documenting AI
  • 10 - Procurement and Shadow AI

3. Culture - Establishing Governance Structures

  • 11 - Connector - From context to culture
  • 12 - Tone at the top
  • 13 - Existing roles
  • 14 - The AI ethics committee
  • 15 - Diversity and stakeholders

4. Content - Managing Data and AI Models

  • 16 - Connector - From culture to content
  • 17 - The big three - Privacy, bias, and explainability
  • 18 - Addressing privacy, bias, and explainability in your AI program
  • 19 - Data done right
  • 20 - Document, document, document
  • 21 - Environmental impacts
  • 22 - A brief word about cybersecurity

5. Commitment

  • 23 - Connector - Moving to commitment
  • 24 - Model drift and monitoring
  • 25 - The role of independent audit
  • 26 - Nurturing a responsible AI culture

Conclusion

  • 27 - The journey of responsible AI

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