Trustworthy AI Software Engineers

AI coding agents should be evaluated as team members, not replacements: The paper conceptualizes AI software engineers as participants in "human-AI SE teams" rather than standalone systems, emphasizing that trustworthiness depends on how well they integrate with human engineers and support collaborative work.

Trustworthiness requires more than technical performance: The authors identify four key dimensions—technical quality, transparency and accountability, epistemic humility, and societal/ethical alignment—suggesting that an AI coding agent's ability to admit uncertainty and explain its reasoning is as important as the code it produces.

We need to inspect the reasoning, not just the output: The paper introduces "evidence-centric inspection," arguing that developers should evaluate the justifications and signals behind AI decisions rather than simply accepting or rejecting final code, fundamentally changing how code review processes should work with AI agents.

Current verification practices are insufficient for AI-assisted development: The paper identifies code review, verification, and validation as "underexplored challenges" in human-AI teams, indicating that existing software engineering practices need rethinking to properly assess trustworthiness in AI-assisted contexts.

Epistemic humility is a critical but often-overlooked requirement: By highlighting that AI agents should demonstrate awareness of their own limitations and uncertainty, the paper suggests trustworthy AI engineers must actively communicate what they don't know, not just what they can do.