From Failure to Alignment: A Requirements Engineering Framework for Machine Learning Systems
arXiv:2606.31589v1 Announce Type: cross Abstract: 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 stak
As AI systems become ubiquitous and influence critical decisions, the need for transparent, trustworthy, and stakeholder-aligned development processes is becoming more urgent.
This framework addresses the fundamental challenge of ensuring AI systems meet societal and organizational needs, moving beyond technical performance to ethical and practical alignment, crucial for long-term adoption and trust.
The focus shifts from merely building functional AI to systematically engineering AI systems that explicitly account for and communicate trustworthiness to diverse stakeholders, integrating 'requirements engineering' into the AI lifecycle.
- · AI ethicists
- · Requirements engineers
- · Organizations deploying critical AI
- · AI governance & regulatory bodies
- · AI developers ignoring stakeholder alignment
- · Organisations with 'black box' AI approaches
- · Purely technically-driven AI development methodologies
Wider adoption of formal requirements engineering processes in AI development becomes a standard.
Increased accountability and transparency in machine learning systems lead to greater public trust and accelerated responsible deployment.
New certification and auditing industries emerge specifically for AI trustworthiness and stakeholder alignment, influencing AI procurement and regulatory landscapes.
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