Bias by Design: Sociological Reflections on AI-Generated Text Detection Failures

The study examines the sociological dimensions of algorithmic bias in AI-generated text detection tools, challenging the widespread perception of these technologies as neutral instruments of academic integrity. While such tools are often portrayed as objective mechanisms for distinguishing human-authored content from AI-generated text, their misclassifications reveal deeper epistemological and structural issues. These misjudgments, particularly when disproportionately targeting non-native English speakers, early-career scholars, and independent or unaffiliated researchers are not mere technical errors but symptomatic of broader socio-technical systems shaped by cultural assumptions, linguistic hierarchies, and institutional power dynamics. By situating AI detection within the sociology of technology, the study draws upon frameworks such as epistemic injustice, symbolic power, and data colonialism to interrogate how algorithmic tools reproduce and legitimize existing academic inequalities. The research relies on qualitative analysis of empirical findings, including documented cases of AI misclassification, peer-reviewed studies, and narratives from affected researchers. These sources collectively demonstrate how detection systems normalize dominant academic standards while marginalizing diverse modes of expression and thought. Moreover, the study argues that these technologies function as gatekeeping mechanisms, not only regulating the boundaries of acceptable knowledge but also reinforcing the epistemic authority of elite institutions and linguistic norms. As AI becomes increasingly embedded in academic evaluation processes, its role in shaping scholarly legitimacy and access to publication must be critically scrutinized. Ultimately, this study calls for a more reflexive and socially informed approach to AI deployment in academia, one that acknowledges and addresses its latent biases and asymmetrical impacts.