Generative AI, Specific Moral Values: A Closer Look at ChatGPT’s New Ethical Implications for Medical AI
Bibliographic record
Abstract
Cohen's (2023) mapping exercise of possible bioethical issues emerging from the use of ChatGPT in medicine provides an informative, useful, and thought-provoking trigger for discussions of AI ethics in health.Yet, he acknowledges that it is not exhaustive.Cohen's analysis carefully considers principles such as privacy and bias prevention, but it does not delve into areas such as explainability, responsibility, or accountability, which are essential to explore.In this commentary we build on Cohen's foundation, adding implications that stem from a distinctive feature of ChatGPT that differentiates it from conventional medical AI tools.In the context of our ongoing research under the NIH-funded Bridge2AI program, we are spearheading the first comprehensive scoping review of ethical and trustworthy medical AI design, employing a bioethical lens to focus on value-based aspects (Victor et al., 2023).Our review spans a broad range of literature, revealing the core moral values that underscore medical AI development.This review enables us to inform a 'value-sensitive design' approach to medical AI.Our work validates Cohen's arguments regarding ChatGPT raising familiar bioethical issues such as bias and privacy, but also brings to light uncharted ethical challenges arising from ChatGPT's status as a general AI model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.011 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".