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

arXiv: https://arxiv.org/abs/2209.04963 · HTML: https://mlsanity.com/paper/2209.04963/

## 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 (most cited first, 30 of 35)

- [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)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020)
- [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020)
- [Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI](https://mlsanity.com/paper/2010.07487/) (2020)
- [Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure](https://mlsanity.com/paper/2010.13561/) (2020)
- [Artificial Intelligence: the global landscape of ethics guidelines](https://mlsanity.com/paper/1906.11668/) (2019)
- [Requirements Engineering for Machine Learning: Perspectives from Data Scientists](https://mlsanity.com/paper/1908.04674/) (2019)
- [Guidelines for including grey literature and conducting multivocal literature reviews in software engineering](https://mlsanity.com/paper/1707.02553/) (2017)
- [Question-Driven Design Process for Explainable AI User Experiences](https://mlsanity.com/paper/2104.03483/) (2021)
- [Principles to Practices for Responsible AI: Closing the Gap](https://mlsanity.com/paper/2006.04707/) (2020)
- [Architectural Patterns for the Design of Federated Learning Systems](https://mlsanity.com/paper/2101.02373/) (2021)
- [AI Certification: Advancing Ethical Practice by Reducing Information Asymmetries](https://mlsanity.com/paper/2105.10356/) (2021)
- [Software Architecture for ML-based Systems: What Exists and What Lies Ahead](https://mlsanity.com/paper/2103.07950/) (2021)
- [Towards Traceability in Data Ecosystems using a Bill of Materials Model](https://mlsanity.com/paper/1904.04253/) (2019)
- [Reflexive Design for Fairness and Other Human Values in Formal Models](https://mlsanity.com/paper/2010.05084/) (2020)
- [AI and Ethics -- Operationalising Responsible AI](https://mlsanity.com/paper/2105.08867/) (2021)
- [German AI Start-Ups and AI Ethics: Using A Social Practice Lens for Assessing and Implementing Socio-Technical Innovation](https://mlsanity.com/paper/2206.09978/) (2022)
- [Developing and Operating Artificial Intelligence Models in Trustworthy Autonomous Systems](https://mlsanity.com/paper/2003.05434/) (2020)
- [FLRA: A Reference Architecture for Federated Learning Systems](https://mlsanity.com/paper/2106.11570/) (2021)
- [Towards a Roadmap on Software Engineering for Responsible AI](https://mlsanity.com/paper/2203.08594/) (2022)
- [Machine Ethics and Automated Vehicles](https://mlsanity.com/paper/2010.15665/) (2020)
- [Prescriptive and Descriptive Approaches to Machine-Learning Transparency](https://mlsanity.com/paper/2204.13582/) (2022)
- [An Empirical Evaluation of GDPR Compliance Violations in Android mHealth Apps](https://mlsanity.com/paper/2008.05864/) (2020)
- [Blockchain-based Trustworthy Federated Learning Architecture](https://mlsanity.com/paper/2108.06912/) (2021)
- [Opening the Software Engineering Toolbox for the Assessment of Trustworthy AI](https://mlsanity.com/paper/2007.07768/) (2020)
- [More Reliable AI Solution: Breast Ultrasound Diagnosis Using Multi-AI Combination](https://mlsanity.com/paper/2101.02639/) (2021)
- [A Decentralized Approach towards Responsible AI in Social Ecosystems](https://mlsanity.com/paper/2102.06362/) (2021)

## Built on by (most cited first, 20 of 20)

- [Towards Responsible Generative AI: A Reference Architecture for Designing Foundation Model based Agents](https://mlsanity.com/paper/2311.13148/) (2023)
- [An AI System Evaluation Framework for Advancing AI Safety: Terminology, Taxonomy, Lifecycle Mapping](https://mlsanity.com/paper/2404.05388/) (2024)
- [Stakeholder Participation for Responsible AI Development: Disconnects Between Guidance and Current Practice](https://mlsanity.com/paper/2506.09873/) (2025)
- [Ethical AI Governance: Methods for Evaluating Trustworthy AI](https://mlsanity.com/paper/2409.07473/) (2024)
- [Toward Effective AI Governance: A Review of Principles](https://mlsanity.com/paper/2505.23417/) (2025)
- [Engaged AI Governance: Addressing the Last Mile Challenge Through Internal Expert Collaboration](https://mlsanity.com/paper/2604.21554/) (2026)
- [PASTA: A Scalable Framework for Multi-Policy AI Compliance Evaluation](https://mlsanity.com/paper/2601.11702/) (2026)
- [Trustworthy AI Posture (TAIP): A Framework for Continuous AI Assurance of Agentic Systems at Horizontal and Vertical scale](https://mlsanity.com/paper/2603.03340/) (2026)
- [Anonymous-by-Construction: An LLM-Driven Framework for Privacy-Preserving Text](https://mlsanity.com/paper/2603.17217/) (2026)
- [To Build or Not to Build? Factors that Lead to Non-Development or Abandonment of AI Systems](https://mlsanity.com/paper/2604.28053/) (2026)
- [A Reference Architecture for Designing Foundation Model based Systems](https://mlsanity.com/paper/2304.11090/) (2023)
- [Achieving Responsible AI through ESG: Insights and Recommendations from Industry Engagement](https://mlsanity.com/paper/2409.10520/) (2024)
- [Privacy in Responsible AI: Approaches to Facial Recognition from Cloud Providers](https://mlsanity.com/paper/2503.04866/) (2025)
- [Perceptions of Agentic AI in Organizations: Implications for Responsible AI and ROI](https://mlsanity.com/paper/2504.11564/) (2025)
- [An Adaptive Responsible AI Governance Framework for Decentralized Organizations](https://mlsanity.com/paper/2510.03368/) (2025)
- [RAD-AI: Rethinking Architecture Documentation for AI-Augmented Ecosystems](https://mlsanity.com/paper/2603.28735/) (2026)
- [Human-AI Coordination Zones: A Framework for Designing Human-in-the-Loop Experiences with Agentic AI](https://mlsanity.com/paper/2606.09848/) (2026)
- [Generative Responsible AI Data Evaluation Schema (GRAIDES) for AI Assurance in Local Government](https://mlsanity.com/paper/2606.20963/) (2026)
- [What We Know about Responsible AI Practices in Industry: A Half Decade of Empirical Research](https://mlsanity.com/paper/2608.10431/) (2026)
- [From Protocols to Evidence: Bounded Claims for AI in Service of the Common Good](https://mlsanity.com/paper/2609.11910/) (2026)

## 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.