- The "Diligence" competency in AI Fluency emphasizes the ethical and safety aspects of AI interaction, ensuring that AI use is responsible, rigorous, transparent, and accountable.
- It requires users to consider the broader implications of AI collaboration, focusing on potential impacts, data access, and alignment with ethical standards, rather than solely on efficiency.
- Diligence is broken down into three interconnected components: creation, transparency, and deployment diligence, guiding responsible AI use from system selection to output sharing.
Lesson 10: A closer look at Diligence | AI Fluency: Framework & Foundations Course
- Understand that AI diligence encompasses ethical and safety considerations, requiring you to take responsibility for all AI interactions beyond just achieving effective or efficient results.
- Practice
Creation Diligenceby critically evaluating AI systems before use; inquire about their training data, ownership, data sharing policies, and how they align with privacy/security standards and organizational policies. - Implement
Transparency Diligenceby openly disclosing AI's role in your work to relevant stakeholders (e.g., colleagues, clients) at an appropriate level of detail to foster trust and informed collaboration. - Exercise
Deployment Diligenceby taking full responsibility for AI-generated outputs you use or share; meticulously verify facts, check for biases, and ensure accuracy, appropriateness, and usage rights. - Develop personal ethical guidelines for AI use and familiarize yourself with organizational policies and industry standards, recognizing that legal and regulatory frameworks for AI are still emerging and evolving.
- Continuously question the implications of working with AI systems, considering who might be affected by the output, who has access to the data, and how interactions align with personal, professional, and organizational values.
AI Fluency Framework — A framework that defines working with AI effectively, efficiently, ethically, and safely.
Diligence Competency — The ability to take responsibility for your AI interactions, ensuring they are rigorous, transparent, and accountable.
Creation Diligence — The ability to be critical and intentional when choosing which AI systems to work with and how you use them.
Transparency Diligence — The ability to be open and accurate about AI interaction with all relevant stakeholders.
Deployment Diligence — The ability to take informed responsibility for the outputs generated or assisted by AI, before using or sharing them.
Ethical Standards — Principles that guide responsible behavior and decision-making, ensuring actions align with moral values.
Data Protection Policies — Rules and guidelines an organization implements to govern how personal or sensitive data is collected, stored, processed, and shared.
Regulatory Frameworks — Systems of laws, rules, and guidelines established by governing bodies to control or supervise an area of activity, such as AI.
In this video, we'll examine the diligence competency from the AI Fluency Framework. Remember that AI fluency means working with AI effectively, efficiently, ethically, and safely. While the other three competencies primarily address effectiveness and efficiency, diligence focuses mostly on the ethical and safety aspects that are just as crucial for successful AI collaboration. But it's hard, diligence is about taking responsibility for your AI interactions. It's the dimension of AI fluency that ensures your use of AI systems is not only productive, but also rigorous, transparent, and accountable. Unlike the other competencies that primarily focus on getting results, diligence asks us to consider broader questions that are nevertheless critical to AI collaboration, particularly in professional environments, such as, what are the implications of working with this AI? Who might be affected by what is created, or 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 aligns with ethical standards and values? 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. Intelligence 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. 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, in other words, 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. As we discussed before, 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 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, 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 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. And 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. To recap, 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. Dilligence reminds us that our interaction with AI comes with responsibilities. To be thoughtful about the systems we choose and about how we work with them. To be honest, about AI's role in our work. And ultimately to be accountable for what we create when working with AI. We all want AI that is fair and safe and of benefit to our society. Our own behaviors play a key role in making this happen.
TL;DR
- The "Diligence" competency in AI Fluency emphasizes the ethical and safety aspects of AI interaction, ensuring that AI use is responsible, rigorous, transparent, and accountable.
- It requires users to consider the broader implications of AI collaboration, focusing on potential impacts, data access, and alignment with ethical standards, rather than solely on efficiency.
- Diligence is broken down into three interconnected components: creation, transparency, and deployment diligence, guiding responsible AI use from system selection to output sharing.
Takeaways
- Understand that AI diligence encompasses ethical and safety considerations, requiring you to take responsibility for all AI interactions beyond just achieving effective or efficient results.
- Practice
Creation Diligenceby critically evaluating AI systems before use; inquire about their training data, ownership, data sharing policies, and how they align with privacy/security standards and organizational policies. - Implement
Transparency Diligenceby openly disclosing AI's role in your work to relevant stakeholders (e.g., colleagues, clients) at an appropriate level of detail to foster trust and informed collaboration. - Exercise
Deployment Diligenceby taking full responsibility for AI-generated outputs you use or share; meticulously verify facts, check for biases, and ensure accuracy, appropriateness, and usage rights. - Develop personal ethical guidelines for AI use and familiarize yourself with organizational policies and industry standards, recognizing that legal and regulatory frameworks for AI are still emerging and evolving.
- Continuously question the implications of working with AI systems, considering who might be affected by the output, who has access to the data, and how interactions align with personal, professional, and organizational values.
Vocabulary
AI Fluency Framework — A framework that defines working with AI effectively, efficiently, ethically, and safely.
Diligence Competency — The ability to take responsibility for your AI interactions, ensuring they are rigorous, transparent, and accountable.
Creation Diligence — The ability to be critical and intentional when choosing which AI systems to work with and how you use them.
Transparency Diligence — The ability to be open and accurate about AI interaction with all relevant stakeholders.
Deployment Diligence — The ability to take informed responsibility for the outputs generated or assisted by AI, before using or sharing them.
Ethical Standards — Principles that guide responsible behavior and decision-making, ensuring actions align with moral values.
Data Protection Policies — Rules and guidelines an organization implements to govern how personal or sensitive data is collected, stored, processed, and shared.
Regulatory Frameworks — Systems of laws, rules, and guidelines established by governing bodies to control or supervise an area of activity, such as AI.
Transcript
In this video, we'll examine the diligence competency from the AI Fluency Framework. Remember that AI fluency means working with AI effectively, efficiently, ethically, and safely. While the other three competencies primarily address effectiveness and efficiency, diligence focuses mostly on the ethical and safety aspects that are just as crucial for successful AI collaboration. But it's hard, diligence is about taking responsibility for your AI interactions. It's the dimension of AI fluency that ensures your use of AI systems is not only productive, but also rigorous, transparent, and accountable. Unlike the other competencies that primarily focus on getting results, diligence asks us to consider broader questions that are nevertheless critical to AI collaboration, particularly in professional environments, such as, what are the implications of working with this AI? Who might be affected by what is created, or 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 aligns with ethical standards and values? 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. Intelligence 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. 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, in other words, 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. As we discussed before, 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 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, 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 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. And 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. To recap, 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. Dilligence reminds us that our interaction with AI comes with responsibilities. To be thoughtful about the systems we choose and about how we work with them. To be honest, about AI's role in our work. And ultimately to be accountable for what we create when working with AI. We all want AI that is fair and safe and of benefit to our society. Our own behaviors play a key role in making this happen.