The Epistemic Limits of NLP Models in Media Bias Detection: Toward a Framework for Context-Aware and Reflexive AI Systems

Abstract

Natural language processing (NLP) models are increasingly used to identify media bias, yet their capacity to capture the interpretive and contextual nature of bias remains fundamentally limited. Most systems conceptualize bias as a linguistic deviation detectable through statistical regularities, overlooking the socio-political and pragmatic dimensions that shape news discourse. This paper examines the epistemic limits of such models and proposes a theoretical framework for context-aware and reflexive AI systems. The framework integrates four epistemic dimensions: representation, contextual grounding, interpretive plurality, and governance, to characterize how meaning and bias co-evolve in communicative contexts. Building on this foundation, we introduce the notion of reflexive AI: systems capable of articulating the provenance, uncertainty, and assumptions underlying their own outputs. Rather than aiming to eliminate bias, reflexive systems make explicit the interpretive processes through which bias is constructed and recognized. This epistemic reframing advances media bias detection from a task of classification toward one of governance, providing a principled basis for designing transparent, accountable, and contextually grounded NLP systems.

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