Generative AI in research funding

Three principles for the use of generative AI in the writing phase of the grant proposal

These three principles are based on the Amsterdam UMC Research Code; Basic Principles of Research Code Amsterdam UMC

1. Accountability and Factual Quality:

Applicants are ultimately responsible for the content of their applications. While GAI can be a supportive tool, it carries risks such as plagiarism, incorrect citations and inaccuracies. Applicants must verify the accuracy, bias, integrity and completeness of AI-generated content and ensure correct citations are added. Generative AI models are not considered authors or co-authors.

2. Data Protection and Confidentiality:

Grant proposals are confidential, and sharing sensitive information with GAI tools can lead to data leaks, potentially affecting future patent rights. Input data, including text and prompts, may be stored and reused by AI applications, risking exposure to other users. Applicants should avoid entering personal data or confidential information into GAI applications.

3. Transparency:

Applicants must be transparent about the use of GAI in their proposals. This includes documenting the use of AI and other language models, validating sources, and mentioning AI use in the references of the application. Funding bodies, such as Horizon Europe, may require explicit acknowledgment of AI use. For internal grants, such as the Amsterdam UMC Postdoc Career Bridging Grant, applicants will be asked to acknowledge the use of generative AI in the application form.

Applicants must respect (inter)national legislation, including the General Data Protection Regulation, and adhere to the Dutch code of conduct for scientific integrity. They should also follow any specific guidelines from their research institutions.

5. Sustainability

The application of generative AI is still in its infancy. It is important to experiment with possible applications, taking into account the associated risks. Another aspect to take into account is the amount of energy needed to work with AI due to the significant computing capacity required. Working with AI is not sustainable. So make sure you have a clear goal in mind when you experiment, and be conscientious.