Disclaimer: No artificial intelligence tools were used in the development of this piece. Using AI to craft a piece about AI usage and application feels, for lack of a better term, disingenuous.
I celebrate Aubrey, Isabella, and the Perspectives editorial team for curating a collection of pieces that delve into the complicated waters of artificial intelligence, its impact on student learning and development, and its impact on our profession’s ability to ‘elevate the standard’ of our work.
Like many of our FSL support operations, AFA is a small, but mighty team. We are a collective community that is asked to punch above our weight in a number of ways. As a small team, we attempt to leverage tools that help us to do our work more efficiently, and with greater focus. AFA leverages AI tools to learn more about our constituents and their interactions with AFA, identify trends in our engagement data, and to better drive and facilitate our interactions with our people (our members, our donors, and our constituents more broadly). But like any tool, it must operate from a shared set of guardrails, especially since it is a tool that can either make our human connections more focused, or render them less valuable.
In order to build some shared practice on our team about the use of AI, AFA has adopted the ‘4D Framework for AI Fluency’ that guides how our team interacts with, utilizes, and prioritizes (or deprioritizes) human-AI interaction (you can learn more about the ‘4D Framework’ from this free course). The 4D Framework creates higher-level guidelines that help our team to drive how artificial intelligence tools can and should be applied in the context of non-profit work — Delegation, Diligence, Description, and Discernment.
- Delegation drives the AFA team to think about what work should be done by humans vs. AI. It helps us to identify what AI usage may enhance or focus our human work, and what we want that work to accomplish, and where there are limitations in that usage. For AFA, this may include leveraging AI tools to make greater sense of our constituency engagement data, but leveraging human interactions to help better explain those trends. AI may help us to identify themes, but those themes are only fully understood through the lenses of our human interactions and real-time human feedback.
- Diligence challenges the AFA team to take full responsibility for the ‘when and how’ of AI usage. This includes careful decisions about tool usage, clear disclosure about usage, and verification about outputs. For AFA, this means that there are clear, shared understandings about which tools we do (and don’t) use, and clarity with our audiences about when and how AI was used to arrive at a final product in any capacity, from grammatical review, to data analysis, to resource development.
- Description is being clear with AI tools in how you want to tackle the problem or question. It means guiding their approach carefully, and maintaining control over the interaction the entire time. For AFA, this means setting guardrails about data privacy, starting with a clear question/problem in mind, and ‘taping AI back into compliance’ when it is making choices that are outside of our explicit instructions.
- Discernment is our commitment to quality control. It means that our team is evaluating the work of AI through a critical lens and reviewing for quality, polish, and professionalism. For AFA, this means that all data outputs are double-checked for accuracy, that any irregularities are explored outside of AI, and that the knowledge of the history, context, and culture of our profession (and our Association) are layered throughout that critical review.
All of this work is centered around the ‘human in the loop’ commitment: that no part of our AI interactions, outputs, or automations are done without the explicit review, direction, and critique of a human. AFA sees AI as a tool that should focus and elevate our human interactions, not replace them. Our work is human-centered, and while technological tools have strengthened the ways in which we can connect with our community, it has never replaced the professional-facing nature of AFA.
As you digest this issue of Perspectives, I invite you to reflect on the ways in which AI usage creates opportunities for more focused interactions with humans (students, collaborators, colleagues), and where having a ‘human in the loop’ is still essential to our collective work.
