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A closer look at Diligence

📖 Lesson content

Estimated Time: 20 minutes

By the end of this lesson, you'll be able to:

  • Understand the ethical implications of AI collaboration
  • Understand the importance of transparency in AI work
  • Recognize your responsibility in AI interactions and outputs

Video: A closer look at diligence

(7 minutes)

This video explores Diligence, the AI Fluency competency that focuses on responsible and ethical AI collaborations. We explain that while the other competencies primarily address effectiveness and efficiency, Diligence addresses ethical and safety aspects that are equally crucial. We introduce three components:

  • Creation Diligence: Being thoughtful about which AI systems you choose and how you work with them
  • Transparency Diligence: Being open about AI's role in your work
  • Deployment Diligence: Taking ownership for AI-assisted outputs you share with others

We emphasize that different contexts may have different expectations, but we each have a responsibility to understand and meet these expectations.

Key takeaways

  • Diligence is about taking responsibility for our AI collaborations
  • Creation Diligence involves being thoughtful about which AI systems we use and how we engage with them
  • Transparency Diligence means being honest about AI's role in our work with everyone who needs to know
  • Deployment Diligence requires taking responsibility for verifying and vouching for the outputs we use or share
  • Different contexts (personal, academic, professional) may have different expectations for disclosure and verification
  • Thoughtful Diligence helps ensure our AI collaborations are not only effective and efficient, but also ethical and safe

Diligence — responsible AI collaboration

3 components

Creation — choose AI thoughtfully

Transparency — disclose AI role

Deployment — verify before sharing

Ethics + safety in AI work

Trust + clear accountability

Exercises

Exercise: Creating a Diligence Statement

Estimated time: 14 minutes

In this exercise, you'll draft a diligence statement for the project you’ve been working on. Here is the diligence statement for this course itself.

Step 1: Understand Diligence Statements

Estimated time: 3 minutes

A diligence statement is a transparent acknowledgment of AI's role in your work, along with your commitment to responsibility for the final output. Here's an example:

"In creating this [document/project/content], I collaborated with [AI assistant name] to assist with [specific tasks: drafting, research, editing, etc.]. I affirm that all AI-generated and co-created content underwent thorough review and evaluation. The final output accurately reflects my understanding, expertise, and intended meaning. While AI assistance was instrumental in the process, I maintain full responsibility for the content, its accuracy, and its presentation. This disclosure is made in the spirit of transparency and to acknowledge the role of AI in the creation process."

Step 2: Reflect on Your AI Collaboration

Estimated time: 5 minutes

Think about your work on the course project and consider:

  • Creation Diligence:
    • Which AI systems did you choose to work with and why?
    • What data or information did you share with the AI?
    • Were there any privacy, security, or ethical considerations in your choices?
  • Transparency Diligence:
    • Who is the audience for your project output?
    • What expectations might they have regarding AI disclosure?
    • How specifically did AI contribute to different aspects of your work?
  • Deployment Diligence:
    • What steps did you take to verify the accuracy and appropriateness of AI contributions?
    • How did you ensure the final output meets your standards and requirements?
    • What responsibility are you taking for the final product?

Step 3: Draft Your Diligence Statement

Estimated time: 6 minutes

Open a conversation with Claude and:

  1. Share your reflections from Step 2, as well as optionally your past conversations that you’ve had with Claude about this project
  2. Collaborate with Claude to draft a diligence statement specific to your project
  3. Ensure your statement addresses:
    • Which AI systems you used
    • How the AI contributed to your project
    • The review process you employed
    • Your assertion of responsibility for the final output
    • Any context-specific considerations (academic, professional, etc.)

Step 4: Add Your Statement to Your Project

When you finish your project, add your diligence statement in an appropriate location (e.g., footer, appendix, or metadata).

Reflection

Before moving on, take a moment to consider:

  • Which aspect of Diligence (Creation, Transparency, or Deployment) do you find most challenging, and why?
  • How might your approach to Diligence vary depending on the context (personal, academic, professional)?
  • How does acknowledging AI's role in your work affect how others might perceive it?
  • What ethical considerations arose during your project that you hadn't anticipated?
  • What personal guidelines might you develop for responsible AI collaboration going forward?

What’s next

In the final lesson of this course, we'll reflect on what we've learned about AI Fluency and how these competencies work together. We'll revisit the framework as a whole and discuss how you can continue developing these skills as AI capabilities evolve. The conclusion will help you synthesize the knowledge and practices you've gained and apply them to future AI collaborations.

Feedback on this course

As you progress through the course, we'd love to hear from you about how you are using concepts from the course in your life, work, or classes and any feedback you may have. Share your feedback here.

🎬 Video transcript

Source video: QbLf2zb3oPc

📜 Click to expand transcript (cleaned + AI-translated)

The Diligence Competency in AI Fluency

AI fluency means working with AI effectively, efficiently, ethically, and safely. While other competencies primarily address effectiveness and efficiency, diligence focuses mostly on the ethical and safety aspects that are crucial for successful AI collaboration.

Diligence is about taking responsibility for your AI interactions. It is the dimension of AI fluency that ensures your use of AI systems is not only productive, but also rigorous, transparent, and accountable. Unlike other competencies that focus on getting results, diligence asks us to consider broader questions critical to professional environments:

  • What are the implications of working with this AI?
  • Who might be affected by what is created, by the collaboration itself, or by any missed inaccuracies?
  • Who has access to the data used to produce this output?
  • How do I ensure that my interaction and the outcome align with ethical standards and values?

The Analogy of Responsible AI Use

Think about it like driving a car. We don't just focus on getting from point A to point B efficiently; we also consider safety, follow traffic rules, and remain aware of how our driving affects others on the road. Similarly, diligence recognizes that AI systems and our interactions with them don't exist in a vacuum. Working with AI responsibly requires awareness of broader contexts and their implications.

Creation Diligence

Diligence begins with becoming more critically thoughtful about which AI systems we work with, how we work with them, and the impacts that come from those collaborations. We should seek answers to questions like:

  • How is this system trained and built?
  • What data was used?
  • Who owns the data I'm inputting right now?
  • Who may have access to it once it's shared?
  • How am I protecting the privacy and security of myself and others?
  • What other impacts does this system have?
  • How does this interaction align with my personal and professional values or with my organization's policies?

For example, before sharing sensitive company information with an AI assistant, it's important to first check whether the service has appropriate data protection policies in place, or if your organization permits such sharing. We call this type of diligence Creation Diligence. It is your ability to be critical and intentional about which AI systems you choose to work with and how you work with them.

Transparency Diligence

Different settings—personal, academic, creative, and professional—may have different expectations of disclosure about AI interaction. However, the responsibility is on each of us to understand and meet these expectations. Ask yourself:

  • Who needs to know about AI's role in this work?
  • How and when should I communicate this?
  • What level of detail makes sense to share?

Meeting expectations for transparency—being forthright and honest—isn't just about following rules and regulations; it's about maintaining trust and respect in your relationships. It acknowledges that people have the right to know when AI has played a significant role in content creation or in decisions that affect them.

For instance, if you used AI to help draft a team proposal, letting your colleagues know which parts were AI-assisted allows for a more honest collaboration and keeps everyone on the same page. We call this Transparency Diligence. It's the ability to be open and accurate about AI interaction with everyone who needs to know.

Deployment Diligence

AI systems can make mistakes. When you share AI-generated content with the world, you—not the AI—are ultimately responsible for its accuracy and appropriateness. This means verifying facts, checking for biases, ensuring accuracy and usage rights, and performing other checks needed so that you can stand behind what you share.

Consider a journalist who uses AI to help draft an article. Before publishing, they would need to verify every fact and source and ensure that the final piece meets every journalistic standard—the same standards that would apply had they written it entirely themselves. We call this Deployment Diligence. It's the ability to take informed responsibility for the outputs that you use or share after they've been created with AI assistance.

Navigating Standards and Evolving Frameworks

Navigating these diligence considerations isn't always straightforward. Different contexts and stakeholders may have different expectations and standards. It helps to develop personal guidelines for working with AI that align with your own ethics and values.

In professional contexts, familiarize yourself with organizational policies and industry standards. Remember that the legal and regulatory frameworks around AI are still emerging and will continue to evolve. Staying informed is an important part of diligence.

Summary of Diligence

Creation, Transparency, and Deployment Diligence work together to form the complete diligence competency. By developing your capacity for diligence, you ensure that your AI use is not only effective and efficient but also ethical and safe.

Diligence reminds us that our interaction with AI comes with responsibilities:

  1. To be thoughtful about the systems we choose and how we work with them.
  2. To be honest about AI's role in our work.
  3. To be accountable for what we create when working with AI.

Our own behaviors play a key role in ensuring that AI is fair, safe, and of benefit to society.

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