A Rapid Review of Responsible AI frameworks: How to guide the development of ethical AI

Most Responsible AI frameworks focus only on the early planning stage: The review found that the majority of existing frameworks concentrate on the Requirements Elicitation phase of software development, leaving later phases like design, implementation, testing, and deployment largely uncovered. This means practitioners lack guidance for translating ethical principles into actual technical decisions throughout most of the development process.

Few frameworks provide practical tools for implementation: While many frameworks exist to guide ethical AI development, very few offer concrete supporting tools to help practitioners actually apply these principles. The tools that do exist are predominantly offered by private companies, potentially limiting accessibility for smaller organizations or public institutions.

No single framework covers all needs: The review reveals there is no comprehensive "one-size-fits-all" framework that addresses all Responsible AI principles while also supporting the full software development lifecycle. This fragmentation means organizations must piece together guidance from multiple sources rather than following a unified approach.

Different stakeholders lack tailored guidance: Existing frameworks do not adequately serve users with varying skill sets and different organizational goals. Technical teams, ethicists, business leaders, and other stakeholders need different types of support, but current frameworks don't effectively bridge these different perspectives and expertise levels.

A critical gap exists in implementation support: The research highlights an urgent need for more comprehensive frameworks that can guide both technical and non-technical stakeholders through real-world Responsible AI projects from conception through deployment.