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

**Authors:** Vita Santa Barletta, Danilo Caivano, Domenico Gigante, Azzurra Ragone  
**arXiv:** [2306.05003](https://arxiv.org/abs/2306.05003)  
**Date:** 2023-06-08  
**Summary page:** https://www.summarizepaper.com/en/paper/2306.05003v1/

## Key points

- The rise of Artificial Intelligence (AI) has raised concerns about ethical principles for its implementation and use.
- Researchers led by Vita Santa Barletta conducted a rapid review of several frameworks providing principles, guidelines, and tools to help practitioners develop and deploy Responsible AI (RAI) applications.
- Most of these frameworks focus only on the Requirements Elicitation phase of the Software Development Life Cycle (SDLC), leaving other phases uncovered.
- Very few frameworks offer supporting tools for practitioners, with most being provided by private companies.
- There is no comprehensive framework encompassing all RAI principles and SDLC phases that can be navigated by users with different skill sets and goals.
- Future work should focus on developing a comprehensive framework that supports both technical and non-technical stakeholders in implementing real-world projects.
- Collaboration between industry, academia, policymakers, and other stakeholders is important in addressing these issues.
- Clear ethical guidelines for AI development are crucial to ensure that it benefits society as a whole while minimizing potential harm.

## Summary

The rise of Artificial Intelligence (AI) and its increasing presence in our daily lives has sparked a growing concern about the ethical principles that should govern its implementation and use. To address these concerns, a team of researchers led by Vita Santa Barletta conducted a rapid review of several frameworks providing principles, guidelines, and tools to help practitioners develop and deploy Responsible AI (RAI) applications. The study found that most of these frameworks focus only on the Requirements Elicitation phase of the Software Development Life Cycle (SDLC), leaving other phases uncovered. Additionally, very few frameworks offer supporting tools for practitioners, with most being provided by private companies. The results revealed that there is no comprehensive framework encompassing all RAI principles and SDLC phases that can be navigated by users with different skill sets and goals. The lack of such a framework highlights the need for more research in this area to ensure that AI is developed ethically and responsibly. The authors suggest that future work should focus on developing a comprehensive framework that supports both technical and non-technical stakeholders in implementing real-world projects. This study provides valuable insights into the current state of RAI frameworks and highlights the need for further research to ensure ethical development and deployment of AI applications. It also underscores the importance of collaboration between industry, academia, policymakers, and other stakeholders in addressing these issues. Ultimately, it is crucial to establish clear ethical guidelines for AI development to ensure that it benefits society as a whole while minimizing potential harm.