Paper #80: Scalable and Interpretable Authorship Attribution for AI Generated Code

As LLMs become everyday creative partners in software development, code is no longer a purely human artefact; it emerges from an increasingly hybrid human-AI process. Understanding who contributed which parts of a codebase has become a foundational question of creative attribution, touching on authorship, trust, and the integrity of co-created work. Yet existing detectors exceed 100M$+$ parameters, are too slow for practical deployment, and offer no insight into what distinguishes human from AI creative contributions. We present NanoDroid, a backbone-free detector with only 5.1M parameters that runs 20 times faster than 125M-parameter UniXcoder (SOTA) and is fully interpretable: per-stream attribution scores explicitly expose which stylistic evidence (token statistics, transition patterns, surface style) distinguishes human from AI authorship in code. On the challenging 4-class DroidCollection benchmark (Human / Machine / Refined / Adversarial), NanoDroid achieves 90.77% weighted F1, within 2.6% of SOTA, while providing the kind of transparent decision decomposition that black-box models cannot. Beyond a detection tool, we argue NanoDroid offers a lens for studying how human and AI creativity differ stylistically, and where they blur.