🤖 AI Summary
Small- and medium-sized enterprises (SMEs) struggle to implement conventional medical AI ethics frameworks due to resource constraints and fast-paced development environments.
Method: This paper proposes the Scalable Agile Framework for Ethics in AI (SAFE-AI), which deeply integrates ethical governance into agile software development. It introduces a novel scenario-based probabilistic analogy mapping mechanism for responsibility quantification, establishes testable metrics for fairness, transparency, and uncertainty management, and incorporates a lightweight ethics review model to support iterative development.
Contribution/Results: Unlike static, compliance-centric approaches, SAFE-AI operationalizes and scales ethical practice through test-driven acceptance criteria, full-lifecycle monitoring, and business-aligned design. Empirical evaluation demonstrates its applicability in organizations lacking dedicated ethics teams, significantly enhancing model trustworthiness and stakeholder confidence.
📝 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.