# Towards a Roadmap on Software Engineering for Responsible AI

arXiv: https://arxiv.org/abs/2203.08594 · HTML: https://mlsanity.com/paper/2203.08594/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 112 references that are in the map.
- First posted on arXiv 9 Mar 2022.

## Builds on (most cited first, 24 of 24)

- [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)
- [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)
- [Differential Privacy and Machine Learning: a Survey and Review](https://mlsanity.com/paper/1412.7584/) (2014)
- [Requirements Engineering for Machine Learning: Perspectives from Data Scientists](https://mlsanity.com/paper/1908.04674/) (2019)
- [Question-Driven Design Process for Explainable AI User Experiences](https://mlsanity.com/paper/2104.03483/) (2021)
- [Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation](https://mlsanity.com/paper/2010.11411/) (2020)
- [Human Perceptions on Moral Responsibility of AI: A Case Study in AI-Assisted Bail Decision-Making](https://mlsanity.com/paper/2102.00625/) (2021)
- [AI Certification: Advancing Ethical Practice by Reducing Information Asymmetries](https://mlsanity.com/paper/2105.10356/) (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)
- [Developing and Operating Artificial Intelligence Models in Trustworthy Autonomous Systems](https://mlsanity.com/paper/2003.05434/) (2020)
- [Software Engineering for Responsible AI: An Empirical Study and Operationalised Patterns](https://mlsanity.com/paper/2111.09478/) (2021)
- [A Study on the Prevalence of Human Values in Software Engineering Publications, 2015-2018](https://mlsanity.com/paper/1907.07874/) (2019)
- [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)
- [How Can Human Values Be Addressed in Agile Methods? A Case Study on SAFe](https://mlsanity.com/paper/2102.12107/) (2021)
- [When is it permissible for artificial intelligence to lie? A trust-based approach](https://mlsanity.com/paper/2103.05434/) (2021)
- [A Methodology for Ethics-by-Design AI Systems: Dealing with Human Value Conflicts](https://mlsanity.com/paper/2010.07610/) (2020)

## Built on by (most cited first, 9 of 9)

- [Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering](https://mlsanity.com/paper/2209.04963/) (2022)
- [Never trust, always verify : a roadmap for Trustworthy AI?](https://mlsanity.com/paper/2206.11981/) (2022)
- [Responsible Design Patterns for Machine Learning Pipelines](https://mlsanity.com/paper/2306.01788/) (2023)
- [Fairness Concerns in App Reviews: A Study on AI-based Mobile Apps](https://mlsanity.com/paper/2401.08097/) (2024)
- [Developing Responsible Chatbots for Financial Services: A Pattern-Oriented Responsible AI Engineering Approach](https://mlsanity.com/paper/2301.05517/) (2023)
- [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)
- [A Gray Literature Study on Fairness Requirements in AI-enabled Software Engineering](https://mlsanity.com/paper/2512.07990/) (2025)
- [Intersectional Fairness in Large Language Models](https://mlsanity.com/paper/2604.20677/) (2026)
- [Value-Preserving Architectures for Agentic AI Systems](https://mlsanity.com/paper/2609.03920/) (2026)

## Abstract (arXiv)

Although AI is transforming the world, there are serious concerns about its ability to behave and make decisions responsibly. Many ethical regulations, principles, and frameworks for responsible AI have been issued recently. However, they are high level and difficult to put into practice. On the other hand, most AI researchers focus on algorithmic solutions, while the responsible AI challenges actually crosscut the entire engineering lifecycle and components of AI systems. To close the gap in operationalizing responsible AI, this paper aims to develop a roadmap on software engineering for responsible AI. The roadmap focuses on (i) establishing multi-level governance for responsible AI systems, (ii) setting up the development processes incorporating process-oriented practices for responsible AI systems, and (iii) building responsible-AI-by-design into AI systems through system-level architectural style, patterns and techniques.