mlsanityData to 14 Oct 2026

Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering

arXiv 2209.04963 ↗First posted 12 Sep 2022cs.AI cs.CY cs.SEcited by 20

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Abstract (arXiv)

Responsible AI is widely considered as one of the greatest scientific challenges of our time and is key to increase the adoption of AI. Recently, a number of AI ethics principles frameworks have been published. However, without further guidance on best practices, practitioners are left with nothing much beyond truisms. Also, significant efforts have been placed at algorithm-level rather than system-level, mainly focusing on a subset of mathematics-amenable ethical principles, such as fairness. Nevertheless, ethical issues can arise at any step of the development lifecycle, cutting across many AI and non-AI components of systems beyond AI algorithms and models. To operationalize responsible AI from a system perspective, in this paper, we present a Responsible AI Pattern Catalogue based on the results of a Multivocal Literature Review (MLR). Rather than staying at the principle or algorithm level, we focus on patterns that AI system stakeholders can undertake in practice to ensure that the developed AI systems are responsible throughout the entire governance and engineering lifecycle. The Responsible AI Pattern Catalogue classifies the patterns into three groups: multi-level governance patterns, trustworthy process patterns, and responsible-AI-by-design product patterns. These patterns provide systematic and actionable guidance for stakeholders to implement responsible AI.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). Builds on 35 of its 135 references that are in the map. First posted on arXiv 12 Sep 2022.

Builds on

35 of 135 references.

References · 35

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  12. 2021Question-Driven Design Process for Explainable AI User Experiencescited by 31
23 more
  1. 2020Principles to Practices for Responsible AI: Closing the Gapcited by 25
  2. 2021Architectural Patterns for the Design of Federated Learning Systemscited by 17
  3. 2021AI Certification: Advancing Ethical Practice by Reducing Information Asymmetriescited by 15
  4. 2021Software Architecture for ML-based Systems: What Exists and What Lies Aheadcited by 13
  5. 2019Towards Traceability in Data Ecosystems using a Bill of Materials Modelcited by 12
  6. 2020Reflexive Design for Fairness and Other Human Values in Formal Modelscited by 11
  7. 2021AI and Ethics -- Operationalising Responsible AIcited by 11
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  9. 2020Developing and Operating Artificial Intelligence Models in Trustworthy Autonomous Systemscited by 10
  10. 2021FLRA: A Reference Architecture for Federated Learning Systemscited by 9
  11. 2022Towards a Roadmap on Software Engineering for Responsible AIcited by 9
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  15. 2021Blockchain-based Trustworthy Federated Learning Architecturecited by 5
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 5; 2026: 10.

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  1. 2023Towards Responsible Generative AI: A Reference Architecture for Designing Foundation Model based Agentscited by 14
  2. 2024An AI System Evaluation Framework for Advancing AI Safety: Terminology, Taxonomy, Lifecycle Mappingcited by 8
  3. 2025Stakeholder Participation for Responsible AI Development: Disconnects Between Guidance and Current Practicecited by 5
  4. 2024Ethical AI Governance: Methods for Evaluating Trustworthy AIcited by 2
  5. 2025Toward Effective AI Governance: A Review of Principlescited by 2
  6. 2026Engaged AI Governance: Addressing the Last Mile Challenge Through Internal Expert Collaborationcited by 2
  7. 2026PASTA: A Scalable Framework for Multi-Policy AI Compliance Evaluationcited by 1
  8. 2026Trustworthy AI Posture (TAIP): A Framework for Continuous AI Assurance of Agentic Systems at Horizontal and Vertical scalecited by 1
  9. 2026Anonymous-by-Construction: An LLM-Driven Framework for Privacy-Preserving Textcited by 1
  10. 2026To Build or Not to Build? Factors that Lead to Non-Development or Abandonment of AI Systemscited by 1
  11. 2023A Reference Architecture for Designing Foundation Model based Systemscited by 0
  12. 2024Achieving Responsible AI through ESG: Insights and Recommendations from Industry Engagementcited by 0
8 more
  1. 2025Privacy in Responsible AI: Approaches to Facial Recognition from Cloud Providerscited by 0
  2. 2025Perceptions of Agentic AI in Organizations: Implications for Responsible AI and ROIcited by 0
  3. 2025An Adaptive Responsible AI Governance Framework for Decentralized Organizationscited by 0
  4. 2026RAD-AI: Rethinking Architecture Documentation for AI-Augmented Ecosystemscited by 0
  5. 2026Human-AI Coordination Zones: A Framework for Designing Human-in-the-Loop Experiences with Agentic AIcited by 0
  6. 2026Generative Responsible AI Data Evaluation Schema (GRAIDES) for AI Assurance in Local Governmentcited by 0
  7. 2026What We Know about Responsible AI Practices in Industry: A Half Decade of Empirical Researchcited by 0
  8. 2026From Protocols to Evidence: Bounded Claims for AI in Service of the Common Goodcited by 0