The INNOVATE framework to foster ethics of artificial intelligence
Bibliographic record
Abstract
ChatGPT, the latest advancement in Artificial Intelligence (AI), represents one of the most advanced and rapidly evolving chatbot technologies to date. Its capability to provide swift and intelligent responses has garnered admiration from scientists and educators globally. Particularly, the healthcare sector stands to gain significantly from the integration of systems like ChatGPT, with benefits including enhanced productivity, reduced expenses, and improved patient outcomes. However, to ensure their equitable and appropriate implementation, it is crucial to address the ethical challenges associated with these technologies. While numerous studies have highlighted these ethical quandaries, there lacks a comprehensive discussion and resolution framework. This review aims to fill this gap by offering a detailed exploration of the ethical concerns associated with using AI tools like ChatGPT in healthcare. This exploration is structured into five main categories: Bias and discrimination, privacy and data security, disinformation and misinformation, autonomy and human interaction, and accountability and responsibility. Additionally, this review discusses the necessity of establishing a clear ethical framework for deploying AI tools in healthcare, introducing the INNOVATE framework. The detailed description and application of the INNOVATE framework aim to promote ethical practices in AI, ensuring a responsible and beneficial integration into healthcare, thereby addressing the identified ethical concerns.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.021 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".