Concern 936 · Working draft · editorial review in progress
Grant-writing and reporting AI
AI-assisted grant writing and reporting can undermine integrity when fluent text invents evidence, exaggerates results or replaces the organization's real account of its work.
Understanding the concern
AI can help organize a draft or make writing clearer. It cannot establish that a program reached people, achieved outcomes or has the capacity promised in an application. Plausible-looking numbers, quotations and citations need checking against actual records and permissions.
NSF's policy makes proposers responsible for accuracy and authenticity, including AI-assisted content; other funders set their own rules. Think of hiring an editor: better prose does not excuse false claims. Staff should keep ownership of the account, follow the funder's disclosure requirements and protect confidential information. Reports should distinguish completed work, estimates and future plans.
An example
A generated grant report turns a proposed activity into a completed achievement that the charity cannot substantiate.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
Evidence and its limits
NSF is a specific funder example, not a universal policy for charities. NCVO describes possible uses; neither source validates any AI-generated claim.
US National Science Foundation · Funder policy
Use of Generative Artificial Intelligence Technology in the NSF Merit Review Process ↗ (opens in a new tab)Proposers remain responsible for accuracy and authenticity; reviewers must protect confidential proposal material. Specific NSF rules, not a policy for all funders.
NCVO · Sector guidance
How AI can help small charities ↗ (opens in a new tab)Discusses practical uses and risks for small charities, including administration and grant applications. Guidance, not measured effectiveness of every suggested use.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
- NIST AI RMF (opens in a new tab)
framework
- ISO/IEC 42001 (opens in a new tab)
standard
A question to discuss
Can every claim in this application or report be traced to real records, and does it follow this funder's rules?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Trustworthiness
Trustworthiness means giving people good reasons to rely on you. It grows when promises, actions, and evidence line up. With AI, a friendly voice or a famous brand is not enough: people need to know what was checked and what could still go wrong.
In practice, keep promises small enough to test, record mistakes, and tell people when the tool's limits affect them. If a service promises human review, make sure a person really has time and authority to review it.
Ask together: What would give the people affected a sound reason to trust this use?
Stewardship
Stewardship means caring for resources and systems that others depend on, including people who will use them later. It looks beyond the benefit of a quick launch.
Plan for maintenance, energy use, data protection, and eventual shutdown. Ask who will look after the tool when the original team moves on and how future users can avoid inheriting hidden costs.
Ask together: What are we responsible for caring for over time?
Prudence
Prudence means using careful judgment before and during action. It balances worthwhile opportunities with the need to avoid harm that could have been anticipated.
Start with a limited trial, set stopping points, and prefer choices that can be undone when uncertainty is high. Review what happens rather than assuming the original plan remains sound.
Ask together: What is a careful next step, and when should we stop or change course?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
About the register and scoring →More ways to explore
Search for more writing
For more specific or recent material, open a search below or ask for a web summary. AI-generated summaries can contain mistakes; open the original articles to check them.