Sustainability of Machine Learning-Enabled Systems: The Machine Learning Practitioner's Perspective

2025-11-02

AI Summary

Machine learning (ML) sustainability encompasses environmental, social, and economic dimensions, yet current practice overemphasizes carbon footprint while neglecting holistic assessment and implementation barriers. Method: This study conducts the first integrated investigation—combining semi-structured interviews (N=32) with a large-scale online survey (N=203)—to systematically examine ML engineers’ perceptions, gaps, and constraints regarding triple-bottom-line sustainability. Contribution/Results: Findings reveal widespread awareness deficits among practitioners, compounded by the absence of standardized sustainability metrics, engineering tooling, and institutional support. We identify critical organizational and infrastructural bottlenecks hindering sustainable ML adoption. Accordingly, we propose three actionable pathways: (1) development of structured, role-specific engineering guidelines; (2) establishment of a multi-dimensional sustainability measurement framework; and (3) advancement of cross-industry policy coordination. This work provides the first empirically grounded, cross-dimensional analysis of ML sustainability challenges and offers concrete, implementable recommendations for operationalizing sustainable ML engineering.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Humans and AI: Human-in-the-loop Machine Learning
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

Software sustainability is a key multifaceted non-functional requirement that encompasses environmental, social, and economic concerns, yet its integration into the development of Machine Learning (ML)-enabled systems remains an open challenge. While previous research has explored high-level sustainability principles and policy recommendations, limited empirical evidence exists on how sustainability is practically managed in ML workflows. Existing studies predominantly focus on environmental sustainability, e.g., carbon footprint reduction, while missing the broader spectrum of sustainability dimensions and the challenges practitioners face in real-world settings. To address this gap, we conduct an empirical study to characterize sustainability in ML-enabled systems from a practitioner's perspective. We investigate (1) how ML engineers perceive and describe sustainability, (2) the software engineering practices they adopt to support it, and (3) the key challenges hindering its adoption. We first perform a qualitative analysis based on interviews with eight experienced ML engineers, followed by a large-scale quantitative survey with 203 ML practitioners. Our key findings reveal a significant disconnection between sustainability awareness and its systematic implementation, highlighting the need for more structured guidelines, measurement frameworks, and regulatory support.

Problem

• Characterizing sustainability in ML systems from practitioners' perspectives
• Investigating sustainability perceptions and implementation practices in ML workflows
• Identifying challenges hindering systematic sustainability adoption in ML development

Innovation

• Conducts qualitative interviews with ML engineers
• Performs large-scale quantitative survey with practitioners
• Characterizes sustainability challenges in ML workflows