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Large generative AI models (GMs) like GPT and DALL-E are trained to generate content for general, wide-ranging purposes. GM content filters are generalized to filter out content which has a risk of harm in many cases, e.g., hate speech. However, prohibited content is not always harmful -- there are instances where generating prohibited content can be beneficial. So, when GMs filter out content, they preclude beneficial use cases along with harmful ones. Which use cases are precluded reflects the values embedded in GM content filtering. Recent work on red teaming proposes methods to bypass GM content filters to generate harmful content. We coin the term green teaming to describe methods of bypassing GM content filters to design for beneficial use cases. We showcase green teaming by: 1) Using ChatGPT as a virtual patient to simulate a person experiencing suicidal ideation, for suicide support training; 2) Using Codex to intentionally generate buggy solutions to train students on debugging; and 3) Examining an Instagram page using Midjourney to generate images of anti-LGBTQ+ politicians in drag. Finally, we discuss how our use cases demonstrate green teaming as both a practical design method and a mode of critique, which problematizes and subverts current understandings of harms and values in generative AI.
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This content will become publicly available on March 18, 2026
Generative Artificial Intelligence in Medicine
The increased capabilities of generative artificial intelligence (AI) have dramatically expanded its possible use cases in medicine. We provide a comprehensive overview of generative AI use cases for clinicians, patients, clinical trial organizers, researchers, and trainees. We then discuss the many challenges—including maintaining privacy and security, improving transparency and interpretability, upholding equity, and rigorously evaluating models—that must be overcome to realize this potential, as well as the open research directions they give rise to.
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- PAR ID:
- 10580635
- Publisher / Repository:
- Annual Review of Biomedical Data Science
- Date Published:
- Journal Name:
- Annual Review of Biomedical Data Science
- ISSN:
- 2574-3414
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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