From Categorical to Contextual: Interpreting High-Risk Classification for Profiling-Based AI Recommender Systems in the EU AI Act

The EU's AI Act creates regulatory tension for recommender systems through its interaction with the GDPR's profiling provisions. This Article identifies a doctrinal challenge: Article 6(2)'s high-risk classification regime, when combined with GDPR Article 4(4), establishes a binary framework potentially inadequate for varied forms of algorithmic influence. Through doctrinal analysis, a "profiling paradox" is identified, manifesting in three dimensions: (1) conflation of data processing activities with behavioral influence mechanisms, (2) categorical treatment that undermines proportionate risk assessment, and (3) regulatory incentives that favor opaque non-profiling systems over transparent personalization. In comparison with the Digital Services Act's graduated approach, the AI Act's categorical treatment appears to overlook opportunities for more nuanced regulation. This Article proposes a "graduated influence'' doctrine for interpreting Article 6(3)'s "significant risk'' threshold through a multi-factor test examining decisional weight, outcome reversibility, transparency, and user agency. This framework seeks to address regulatory challenges while aligning with fundamental rights objectives.