# Responsible-AI-by-Design: a Pattern Collection for Designing Responsible AI Systems

arXiv: https://arxiv.org/abs/2203.00905 · HTML: https://mlsanity.com/paper/2203.00905/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 2 Mar 2022.

## Builds on (most cited first, 6 of 6)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Software Engineering for Responsible AI: An Empirical Study and Operationalised Patterns](https://mlsanity.com/paper/2111.09478/) (2021)
- [A Decentralized Approach towards Responsible AI in Social Ecosystems](https://mlsanity.com/paper/2102.06362/) (2021)
- [Providing Assurance and Scrutability on Shared Data and Machine Learning Models with Verifiable Credentials](https://mlsanity.com/paper/2105.06370/) (2021)

## Built on by (most cited first, 4 of 4)

- [A Taxonomy of Foundation Model based Systems through the Lens of Software Architecture](https://mlsanity.com/paper/2305.05352/) (2023)
- [Design Patterns for AI-based Systems: A Multivocal Literature Review and Pattern Repository](https://mlsanity.com/paper/2303.13173/) (2023)
- [Developing Responsible Chatbots for Financial Services: A Pattern-Oriented Responsible AI Engineering Approach](https://mlsanity.com/paper/2301.05517/) (2023)
- [Distributed Trust Through the Lens of Software Architecture](https://mlsanity.com/paper/2306.08056/) (2023)

## Abstract (arXiv)

Although AI has significant potential to transform society, there are serious concerns about its ability to behave and make decisions responsibly. Many ethical regulations, principles, and guidelines for responsible AI have been issued recently. However, these principles are high-level and difficult to put into practice. In the meantime much effort has been put into responsible AI from the algorithm perspective, but they are limited to a small subset of ethical principles amenable to mathematical analysis. Responsible AI issues go beyond data and algorithms and are often at the system-level crosscutting many system components and the entire software engineering lifecycle. Based on the result of a systematic literature review, this paper identifies one missing element as the system-level guidance - how to design the architecture of responsible AI systems. We present a summary of design patterns that can be embedded into the AI systems as product features to contribute to responsible-AI-by-design.