mlsanityData to 14 Oct 2026

Machine Learning with Requirements: a Manifesto

arXiv 2304.03674 ↗First posted 7 Apr 2023cs.LG cs.AI cs.SEcited by 8

Not on the map. Why?

Abstract (arXiv)

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 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. We show how the requirements specification can be fruitfully integrated into the standard machine learning development pipeline, proposing a novel pyramid development process in which requirements definition may impact all the subsequent phases in the pipeline, and viceversa.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 37 of its 92 references that are in the map. First posted on arXiv 7 Apr 2023.

Builds on

37 of 92 references.

References · 37

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25 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 3; 2026: 2.

8 papers.

  1. 2024How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Datacited by 29
  2. 2025Beyond the convexity assumption: Realistic tabular data generation under quantifier-free real linear constraintscited by 18
  3. 2025Right for the Right Reasons: Avoiding Reasoning Shortcuts via Prototypical Neurosymbolic AIcited by 3
  4. 2024PiShield: A PyTorch Package for Learning with Requirementscited by 2
  5. 2024ROAD-Waymo: A Large-Scale Action Awareness Dataset for Autonomous Drivingcited by 1
  6. 2026Concise and Logically Consistent Conformal Sets for Neuro-Symbolic Concept-Based Modelscited by 1
  7. 2025Is PMBOK Guide the Right Fit for AI? Re-evaluating Project Management in the Face of Artificial Intelligence Projectscited by 0
  8. 2026A TTP by TTP Approach: Precise Malware Detection via Malicious TTP Recognitioncited by 0