A Practical SAFE-AI Framework for Small and Medium-Sized Enterprises Developing Medical Artificial Intelligence Ethics Policies
Journal:
arXiv
Published Date:
Jul 2, 2025
Abstract
Artificial intelligence (AI) offers incredible possibilities for patient
care, but raises significant ethical issues, such as the potential for bias.
Powerful ethical frameworks exist to minimize these issues, but are often
developed for academic or regulatory environments and tend to be comprehensive
but overly prescriptive, making them difficult to operationalize within
fast-paced, resource-constrained environments. We introduce the Scalable Agile
Framework for Execution in AI (SAFE-AI) designed to balance ethical rigor with
business priorities by embedding ethical oversight into standard Agile-based
product development workflows. The framework emphasizes the early establishment
of testable acceptance criteria, fairness metrics, and transparency metrics to
manage model uncertainty, while also promoting continuous monitoring and
re-evaluation of these metrics across the AI lifecycle. A core component of
this framework are responsibility metrics using scenario-based probability
analogy mapping designed to enhance transparency and stakeholder trust. This
ensures that retraining or tuning activities are subject to lightweight but
meaningful ethical review. By focusing on the minimum necessary requirements
for responsible development, our framework offers a scalable, business-aligned
approach to ethical AI suitable for organizations without dedicated ethics
teams.
