We Optimized the Models and Broke the Community: Social Awareness as a Missing Requirement in ML-Enabled Software Engineering

Abstract

Machine learning (ML) and large language models are increasingly integrated into software engineering (SE) workflows, supporting tasks such as code generation, documentation, testing, and maintenance. While these techniques provide measurable productivity benefits, their impact on the social structure of development teams remains largely underexplored. Existing research primarily focuses on model performance, leaving socio-technical consequences such as fairness, collaboration, and organizational sustainability insufficiently addressed. This keynote positions ML-enabled software engineering as a socio-technical system in which intelligent tools influence communication, coordination, and decision-making. Drawing on empirical studies on fairness-aware practices, community smells, and social debt, we identify two key challenges. First, fairness concerns propagate across the entire ML lifecycle, requiring lifecycle-wide mitigation strategies rather than post-hoc corrections. Second, ML-based assistants reshape collaboration structures, potentially introducing new coordination issues and amplifying existing socio-technical anti-patterns. The goal of the talk is to establish social awareness as a first-class requirement for ML-enabled SE. We outline a research agenda centered on socio-technical metrics, human-centered AI tools, and governance mechanisms to support sustainable and collaborative AI-enabled development ecosystems.

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