Machine learning with requirements: A manifesto

Abstract

In the recent years, machine learning has made great advancements that have been at the root of many breakthroughs in different application domains. However, it is still an open issue how to make them applicable to high-stakes or safety-critical application domains, as they can often be brittle and unreliable. In this paper, we argue that requirements definition and satisfaction can go a long way to make machine learning models even more fitting to the real world, especially in critical domains. To this end, we present two problems in which (i) requirements arise naturally, (ii) machine learning models are or can be fruitfully deployed, and (iii) neglecting the requirements can have dramatic consequences. Our proposed pyramid development process integrates requirements specification into every stage of the machine learning pipeline, ensuring mutual influence between requirements and subsequent phases. Additionally, we explore the pivotal role of Neuro-symbolic AI in facilitating this integration, paving the way for more reliable and robust machine learning applications in critical domains. Through this approach, we aim to bridge the gap between theoretical advancements and practical implementations, ensuring machine learning’s safe and effective deployment in sensitive areas.