# Requirements Engineering for Machine Learning: A Review and Reflection

arXiv: https://arxiv.org/abs/2210.00859 · HTML: https://mlsanity.com/paper/2210.00859/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 55 of its 193 references that are in the map.
- First posted on arXiv 3 Oct 2022.

## Builds on (most cited first, 30 of 55)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020)
- [Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems](https://mlsanity.com/paper/1903.12394/) (2019)
- [Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems](https://mlsanity.com/paper/2003.04919/) (2020)
- [Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure](https://mlsanity.com/paper/2010.13561/) (2020)
- [Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities](https://mlsanity.com/paper/2111.06420/) (2021)
- [Taxonomy of Real Faults in Deep Learning Systems](https://mlsanity.com/paper/1910.11015/) (2019)
- [How do Data Science Workers Collaborate? Roles, Workflows, and Tools](https://mlsanity.com/paper/2001.06684/) (2020)
- [Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support](https://mlsanity.com/paper/2112.05675/) (2021)
- [Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process](https://mlsanity.com/paper/2110.10234/) (2021)
- [Requirements Engineering for Machine Learning: Perspectives from Data Scientists](https://mlsanity.com/paper/1908.04674/) (2019)
- [Investigating Explainability of Generative AI for Code through Scenario-based Design](https://mlsanity.com/paper/2202.04903/) (2022)
- [Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology](https://mlsanity.com/paper/2003.05155/) (2020)
- [Model-based Exploration of the Frontier of Behaviours for Deep Learning System Testing](https://mlsanity.com/paper/2007.02787/) (2020)
- [Safely Entering the Deep: A Review of Verification and Validation for Machine Learning and a Challenge Elicitation in the Automotive Industry](https://mlsanity.com/paper/1812.05389/) (2018)
- [Adoption and Effects of Software Engineering Best Practices in Machine Learning](https://mlsanity.com/paper/2007.14130/) (2020)
- [Scalable Quantitative Verification For Deep Neural Networks](https://mlsanity.com/paper/2002.06864/) (2020)
- [How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study](https://mlsanity.com/paper/2101.06098/) (2021)
- [DeepHyperion: Exploring the Feature Space of Deep Learning-Based Systems through Illumination Search](https://mlsanity.com/paper/2107.06997/) (2021)
- [RobOT: Robustness-Oriented Testing for Deep Learning Systems](https://mlsanity.com/paper/2102.05913/) (2021)
- [Requirements Engineering Challenges in Building AI-Based Complex Systems](https://mlsanity.com/paper/1908.11791/) (2019)
- [Easy over Hard: A Case Study on Deep Learning](https://mlsanity.com/paper/1703.00133/) (2017)
- [An Empirical Study on Deployment Faults of Deep Learning Based Mobile Applications](https://mlsanity.com/paper/2101.04930/) (2021)
- [Beyond Human-Level Accuracy: Computational Challenges in Deep Learning](https://mlsanity.com/paper/1909.01736/) (2019)
- [Self-Checking Deep Neural Networks in Deployment](https://mlsanity.com/paper/2103.02371/) (2021)
- [Exploring Explainability: A Definition, a Model, and a Knowledge Catalogue](https://mlsanity.com/paper/2108.03012/) (2021)
- [Trust in AI: Interpretability is not necessary or sufficient, while black-box interaction is necessary and sufficient](https://mlsanity.com/paper/2202.05302/) (2022)
- [Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models](https://mlsanity.com/paper/2102.00036/) (2021)
- [Developing and Operating Artificial Intelligence Models in Trustworthy Autonomous Systems](https://mlsanity.com/paper/2003.05434/) (2020)
- [White-Box Analysis over Machine Learning: Modeling Performance of Configurable Systems](https://mlsanity.com/paper/2101.05362/) (2021)

## Built on by (most cited first, 7 of 7)

- [Challenges and Opportunities of Using Transformer-Based Multi-Task Learning in NLP Through ML Lifecycle: A Survey](https://mlsanity.com/paper/2308.08234/) (2023)
- [A Mathematical Model of the Hidden Feedback Loop Effect in Machine Learning Systems](https://mlsanity.com/paper/2405.02726/) (2024)
- [Verifying Machine Learning Interpretability Requirements through Provenance](https://mlsanity.com/paper/2604.21599/) (2026)
- [A Guide to Failure in Machine Learning: Reliability and Robustness from Foundations to Practice](https://mlsanity.com/paper/2503.00563/) (2025)
- [Is PMBOK Guide the Right Fit for AI? Re-evaluating Project Management in the Face of Artificial Intelligence Projects](https://mlsanity.com/paper/2506.02214/) (2025)
- [Boosting AI Reliability with an FSM-Driven Streaming Inference Pipeline: An Industrial Case](https://mlsanity.com/paper/2603.01528/) (2026)
- [From Failure to Alignment: A Requirements Engineering Framework for Machine Learning Systems](https://mlsanity.com/paper/2606.31589/) (2026)

## Abstract (arXiv)

Today, many industrial processes are undergoing digital transformation, which often requires the integration of well-understood domain models and state-of-the-art machine learning technology in business processes. However, requirements elicitation and design decision making about when, where and how to embed various domain models and end-to-end machine learning techniques properly into a given business workflow requires further exploration. This paper aims to provide an overview of the requirements engineering process for machine learning applications in terms of cross domain collaborations. We first review the literature on requirements engineering for machine learning, and then go through the collaborative requirements analysis process step-by-step. An example case of industrial data-driven intelligence applications is also discussed in relation to the aforementioned steps.