Lesson 10: A closer look at Diligence | AI Fluency: Framework & Foundations Course

AI Fluency Framework: Diligence Competency

Key Concepts:

• AI Fluency: Working with AI effectively, efficiently, ethically, and safely.
• Diligence: Taking responsibility for AI interactions, ensuring rigorous, transparent, and accountable AI use.
• Creation Diligence: Being critical and intentional about which AI systems to use and how to use them.
• Transparency Diligence: Being open and accurate about AI interaction with everyone who needs to know.
• Deployment Diligence: Taking informed responsibility for the outputs used or shared after AI assistance.

1. Introduction to Diligence

• Diligence is a core competency within the AI fluency framework, focusing on the ethical and safety aspects of AI collaboration.
• It complements the other competencies (which emphasize effectiveness and efficiency) by addressing broader implications of AI use.
• Diligence requires considering the impact of AI interactions, potential effects on stakeholders, data access, and alignment with ethical standards.
• Analogy: Driving a car – diligence is like following traffic rules and being aware of how your driving affects others, not just getting to the destination efficiently.

2. Critical Thinking and AI System Selection (Creation Diligence)

• Diligence starts with critically evaluating AI systems:
  • How was the system trained and built?
  • What data was used?
  • Who owns the data being inputted?
  • Who has access to the data?
  • How is privacy and security protected?
  • What other impacts does the system have?
  • Does the interaction align with personal/professional values and organizational policies?
• Example: Before sharing sensitive company information with an AI assistant, verify the service's data protection policies and organizational permissions.
• Creation diligence is about being intentional and critical about AI system selection and usage.
• Different settings (personal, academic, creative, professional) may have different expectations for AI interaction disclosure.

3. Transparency and Disclosure (Transparency Diligence)

• Transparency diligence involves being open and accurate about AI's role in a project or decision.
• Responsibility lies with the user to understand and meet transparency expectations.
• Key questions:
  • Who needs to know about AI's role?
  • How and when should this be communicated?
  • What level of detail is appropriate?
• Transparency is about maintaining trust and respect by acknowledging AI's role in content creation or decisions.
• Example: Informing colleagues about AI assistance in drafting a team proposal allows for more honest collaboration.

4. Accountability for AI Outputs (Deployment Diligence)

• Users are ultimately responsible for the accuracy and appropriateness of AI-generated content they share.
• Deployment diligence involves verifying facts, checking for biases, ensuring accuracy, and confirming usage rights.
• Example: A journalist using AI to draft an article must verify every fact and source to meet journalistic standards.
• Deployment diligence ensures users can stand behind the content they share, even if AI-assisted.

5. Navigating Diligence and Staying Informed

• Diligence considerations can be complex due to varying expectations and standards.
• Developing personal guidelines for AI use that align with ethics and values is crucial.
• Familiarize yourself with organizational policies and industry standards.
• The legal and regulatory frameworks around AI are evolving, so staying informed is essential.

6. Synthesis/Conclusion

• Creation, transparency, and deployment diligence work together to form the complete diligence competency.
• Diligence ensures AI use is effective, efficient, ethical, and safe.
• It emphasizes thoughtfulness about AI systems, honesty about AI's role, and accountability for AI-assisted creations.
• Our behaviors play a key role in ensuring AI is fair, safe, and beneficial to society.