From Failure to Alignment: A Requirements Engineering Framework for Machine Learning Systems
For practitioners and researchers in MLS development, it provides a systematic process to ensure stakeholder alignment, but the contribution is incremental as it adapts existing RE methods to MLS without empirical validation.
The paper proposes REAL, a requirements engineering framework for developing machine learning systems that align with stakeholder needs, demonstrated via an autonomous driving example. The framework integrates data, model, and system requirements, uses failure to explore alternatives, and supports iterative refinement.
Organisations designing, developing, and deploying machine learning systems (MLS) need to be able to check that these systems are trustworthy, and communicate this clearly to their stakeholders, be they different categories of users, engineers, or wider society. By focusing on stakeholders, Requirements Engineering is well positioned to drive the design and engineering of MLS that align with the needs of their stakeholders. Yet, we still need a systematic process for modelling and reasoning about requirements for MLS that is driven both by stakeholders' needs and constraints for MLS development. This paper proposes a framework entitled REAL (Requirements Engineering for mAchines that Learn - and Fail) to help develop MLS that align with stakeholders' needs by adopting a requirements engineering approach. This model-based framework is based on three principles. First, weaving together requirements for data, models, and the system as a whole. Second, using failure to drive the exploration of alternative requirements. Third, iterative and traceable refinement of MLS requirements. We demonstrate the proposed framework using an example from autonomous driving and show that REAL supports the development of MLS that better align with stakeholders' requirements. A replication package is available online.