Illegal content reporting mechanisms are a key technical and organizational measure through which online platforms address illegal content under the European Union Digital Services Act (DSA). Article 16 requires user notices to be sufficiently substantiated and submitted in good faith, placing users in the difficult position of interpreting legal and procedural language and translating ambiguous content into legally meaningful categories and reasons. We investigate how large language model (LLM)-based assistants can support this reporting process. In a controlled user study (N = 450) using an interface modeled on a major platform reporting workflow, we compare three conditions: unaided reporting, a conventional explainable AI assistant (XAI) that suggests a single legal category with a rationale, and an evaluative AI assistant (EvalAI) that presents balanced pro and con arguments across candidate legal provisions. We further examine these assistance forms under systematically varied AI error regimes. Our results show that EvalAI improves provision-level accuracy under AI error and reduces misclassification distance relative to conventional XAI, particularly for near-miss and overbreadth errors. When AI output is correct, conventional XAI enables faster decisions, but neither AI assistance form reliably improves the quality of users' substantiated explanations relative to unaided reporting. We discuss design implications for compliance-oriented reporting interfaces, highlighting trade-offs between accuracy, deliberation, explanation quality, and vulnerability to misleading AI output.

翻译：非法内容报告机制是在欧盟《数字服务法》（DSA）框架下，在线平台应对非法内容的关键技术与组织措施。第16条要求用户通知需提供充分证据并基于善意提交，这使得用户面临解释法律与程序性语言、将模糊内容转化为具有法律意义的类别及理由的困境。本研究探讨了基于大型语言模型（LLM）的辅助系统如何支持这一报告流程。通过一项以主流平台报告流程为模型构建界面的受控用户研究（N=450），我们比较了三种条件：无辅助报告、传统可解释AI辅助（XAI，提供单一法律类别及理由建议）以及评估型AI辅助（EvalAI，呈现跨候选法律条款的平衡正反论点）。我们进一步在系统化变化的AI错误模式下考察了这些辅助形式。结果表明，与传统XAI相比，EvalAI在AI出错时提升了条款级准确性，并降低了误分类距离，尤其在近似错误和过度宽泛错误场景中表现显著。当AI输出正确时，传统XAI可缩短决策时间，但两种AI辅助形式均未可靠提升用户提供充分论证解释的质量（相较于无辅助报告）。我们讨论了面向合规性报告界面的设计启示，强调了准确性、审慎性、解释质量与对误导性AI输出脆弱性之间的权衡。