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@article{210721,
        author = {Raja Sree Kokkiligadda and Dr. Bonthu Kotaiah},
        title = {Alfgorithmic Bureaucracy and Epistemic Injustice: Quantifying Socio-Linguistic Triage Bias in e-Governance LLM Intake Pipelines},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {6},
        pages = {2171-2178},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=210721},
        abstract = {Modern e-Governance systems increasingly turn to large language model pipelines to triage citizen grievances. However, this introduces socio-linguistic bias. This paper explores whether such systems engage in epistemic marginalization by prioritizing vernacular, code-mixed or dialect specific complaints with lower credibility scores than formal bureaucratic prose. Drawing upon epistemic injustice and algorithmic bureaucracy, we develop a counterfactual evaluation framework which quantifies priority divergence (P) and testimonial deficit (Auth). We validate our approach on five municipal domains, showing that non-elite registers systematically face higher administrative neglect. The findings demonstrate priority downgrading and routing discordance, enabling identification of algorithmic gatekeeping and necessary sociotechnical guardrails to ensure semantic equity in public administration artificial intelligence.},
        keywords = {Governance, Epistemic Injustice, Socio-Linguistic Bias, Large Language Models (LLMs), Algorithmic Bureaucracy, Semantic Equity.},
        month = {November},
        }