mlsanityData to 7 Oct 2026

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

arXiv 2203.00905 ↗First posted 2 Mar 2022cs.AIcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 6 of its 20 references that are in the map. First posted on arXiv 2 Mar 2022.

Builds on

6 of 20 references.

References · 6

  1. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  3. 2018LEAF: A Benchmark for Federated Settingscited by 780
  4. 2021Software Engineering for Responsible AI: An Empirical Study and Operationalised Patternscited by 9
  5. 2021A Decentralized Approach towards Responsible AI in Social Ecosystemscited by 3
  6. 2021Providing Assurance and Scrutability on Shared Data and Machine Learning Models with Verifiable Credentialscited by 3

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Citing papers per year: 2023: 4.

4 papers.