Improving Machine Learning Workflows Using the “Normative‐Descriptive‐Prescriptive” Decision Framework

• Published in: Applied AI letters Vol. 6; no. 2
• Main Authors: Gudigantala, Naveen, Pradhan, Manaranjan, Vemprala, Naga
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Publishing Ltd 01.06.2025; Wiley
• Subjects: artificial intelligence; decision framework; improving ML workflows; machine learning
• ISSN: 2689-5595; 2689-5595
• DOI: 10.1002/ail2.118

ABSTRACT To maximize business value from artificial intelligence and machine learning (ML) systems, understanding what leads to the effective development and deployment of ML systems is crucial. While prior research primarily focused on technical aspects, important issues related to improving decision‐making across ML workflows have been overlooked. This paper introduces a “normative‐descriptive‐prescriptive” decision framework to address this gap. Normative guidelines outline best practices, descriptive dimensions describe actual decision‐making, and prescriptive elements provide recommendations to bridge gaps. The three‐step framework analyzes decision‐making in key ML pipeline phases, identifying gaps and offering prescriptions for improved model building. Key descriptive findings include rushed problem‐solving with convenient data, use of inaccurate success metrics, underestimation of downstream impacts, limited roles of subject matter experts, use of non‐representative data samples, prioritization of prediction over explanation, lack of formal verification processes, and challenges in monitoring production models. The paper highlights biases, incentive issues, and systematic disconnects in decision‐making across the ML pipeline as contributors to descriptive shortcomings. Practitioners can use the framework to pinpoint gaps, develop prescriptive interventions, and build higher quality, ethical, and legally compliant ML systems. This paper introduces a “normative‐descriptive‐prescriptive” framework to analyze decision‐making processes within machine learning (ML) workflows, aiming to maximize business value. By identifying discrepancies between ideal practices and actual decisions, the framework reveals common pitfalls such as data bias, misaligned metrics, and inadequate validation, ultimately guiding practitioners toward building higher‐quality and more ethical ML systems.