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

arXiv: https://arxiv.org/abs/2306.05003 · HTML: https://mlsanity.com/paper/2306.05003/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 29 references that are in the map.
- First posted on arXiv 8 Jun 2023.

## Builds on (most cited first, 3 of 3)

- [The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation](https://mlsanity.com/paper/1802.07228/) (2018)
- [Principles alone cannot guarantee ethical AI](https://mlsanity.com/paper/1906.06668/) (2019)
- [Socially Responsible AI Algorithms: Issues, Purposes, and Challenges](https://mlsanity.com/paper/2101.02032/) (2021)

## Built on by (most cited first, 5 of 5)

- [Modulating Language Model Experiences through Frictions](https://mlsanity.com/paper/2407.12804/) (2024)
- [Trustworthy AI in practice: an analysis of practitioners' needs and challenges](https://mlsanity.com/paper/2407.12135/) (2024)
- [Towards an Operational Responsible AI Framework for Learning Analytics in Higher Education](https://mlsanity.com/paper/2410.05827/) (2024)
- [Quantifying Security Vulnerabilities: A Metric-Driven Security Analysis of Gaps in Current AI Standards](https://mlsanity.com/paper/2502.08610/) (2025)
- [TRUST-ESD: A Risk-Calibrated and Governance-Aware AI Framework for Enterprise Strategic Decision Support Under Uncertainty](https://mlsanity.com/paper/2607.20065/) (2026)

## Abstract (arXiv)

In the last years, the raise of Artificial Intelligence (AI), and its pervasiveness in our lives, has sparked a flourishing debate about the ethical principles that should lead its implementation and use in society. Driven by these concerns, we conduct a rapid review of several frameworks providing principles, guidelines, and/or tools to help practitioners in the development and deployment of Responsible AI (RAI) applications. We map each framework w.r.t. the different Software Development Life Cycle (SDLC) phases discovering that most of these frameworks fall just in the Requirements Elicitation phase, leaving the other phases uncovered. Very few of these frameworks offer supporting tools for practitioners, and they are mainly provided by private companies. Our results reveal that there is not a "catching-all" framework supporting both technical and non-technical stakeholders in the implementation of real-world projects. Our findings highlight the lack of a comprehensive framework encompassing all RAI principles and all (SDLC) phases that could be navigated by users with different skill sets and with different goals.