This study investigates whether professional translators can reliably identify short stories generated in Italian by artificial intelligence (AI) without prior specialized training. Sixty-nine translators took part in an in-person experiment, where they assessed three anonymized short stories - two written by ChatGPT-4o and one by a human author. For each story, participants rated the likelihood of AI authorship and provided justifications for their choices. While average results were inconclusive, a statistically significant subset (16.2%) successfully distinguished the synthetic texts from the human text, suggesting that their judgements were informed by analytical skill rather than chance. However, a nearly equal number misclassified the texts in the opposite direction, often relying on subjective impressions rather than objective markers, possibly reflecting a reader preference for AI-generated texts. Low burstiness and narrative contradiction emerged as the most reliable indicators of synthetic authorship, with unexpected calques, semantic loans and syntactic transfer from English also reported. In contrast, features such as grammatical accuracy and emotional tone frequently led to misclassification. These findings raise questions about the role and scope of synthetic-text editing in professional contexts.

翻译：本研究探讨专业译者在未经专门训练的情况下，能否可靠地识别由人工智能（AI）生成的意大利语短篇小说。69名译者参与了一项现场实验，评估了三篇匿名短篇小说——其中两篇由ChatGPT-4o生成，一篇由人类作者创作。针对每篇故事，参与者评估了AI作者身份的可能性并提供了判断依据。虽然平均结果尚无定论，但一个具有统计学意义的子集（16.2%）成功区分了合成文本与人类文本，表明其判断基于分析能力而非偶然。然而，几乎同等数量的译者做出了相反方向的错误分类，往往依赖主观印象而非客观标记，这可能反映了读者对AI生成文本的偏好。低突发性和叙事矛盾成为合成作者身份最可靠的指标，同时报告了来自英语的意外仿译、语义借用和句法迁移现象。相比之下，语法准确性和情感基调等特征常导致误判。这些发现对专业语境中合成文本编辑的作用与范围提出了新的思考。