Trustworthy AI Software Engineers

TL;DR

This paper redefines AI software engineers as trustworthy agents within human-AI teams, emphasizing key trust dimensions like transparency, ethics, and accountability, and discusses evaluation and governance challenges.

Contribution

It introduces a conceptual framework for trustworthy AI software engineers, integrating technical, ethical, and societal aspects, and highlights trust measurement gaps and design implications.

Findings

Trustworthiness involves technical quality, transparency, and societal alignment.

Trust measurement is complex and cannot be fully quantified.

Ethics-by-design is essential for trustworthy AI SE systems.

Abstract

With the rapid rise of AI coding agents, the fundamental premise of what it means to be a software engineer is in question. In this vision paper, we re-examine what it means for an AI agent to be considered a software engineer and then critically think about what makes such an agent trustworthy. \textit{Grounded} in established definitions of software engineering (SE) and informed by recent research on agentic AI systems, we conceptualise AI software engineers as participants in human-AI SE teams composed of human software engineers and AI models and tools, and we distinguish trustworthiness as a key property of these systems and actors rather than a subjective human attitude. Based on historical perspectives and emerging visions, we identify key dimensions that contribute to the trustworthiness of AI software engineers, spanning technical quality, transparency and accountability, epistemic humility, and societal and ethical alignment. We further discuss how trustworthiness can be evaluated and demonstrated, highlighting a fundamental trust measurement gap: not everything that matters for trust can be easily measured. Finally, we outline implications for the design, evaluation, and governance of AI SE systems, advocating for an ethics-by-design approach to enable appropriate trust in future human-AI SE teams.