AI Safety in the Eyes of the Downstream Developer: A First Look at Concerns, Practices, and Challenges

Abstract

Pre-trained models (PTMs) have become a cornerstone of AI-based software, allowing for rapid integration and development with minimal training overhead. However, their adoption also introduces unique safety challenges, such as data leakage and biased outputs, that demand rigorous handling by downstream developers. While previous research has proposed taxonomies of AI safety concerns and various mitigation strategies, how downstream developers address these issues remains unexplored. This study investigates downstream developers' concerns, practices and perceived challenges regarding AI safety issues during AI-based software development. To achieve this, we conducted a mixed-method study, including interviews with 18 participants, a survey of 86 practitioners, and an analysis of 874 AI incidents from the AI Incident Database. Our results reveal that while developers generally demonstrate strong awareness of AI safety concerns, their practices, especially during the preparation and PTM selection phases, are often inadequate. The lack of concrete guidelines and policies leads to significant variability in the comprehensiveness of their safety approaches throughout the development lifecycle, with additional challenges such as poor documentation and knowledge gaps, further impeding effective implementation. Based on our findings, we offer suggestions for PTM developers, AI-based software developers, researchers, and policy makers to enhance the integration of AI safety measures.

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