Towards Intelligent Government Grievance Redressal Systems: A Survey of Computational Methods and System Architectures

Abstract

Citizen grievance redressal has long suffered from systemicinefficienciesrootedinmanualtriage,opaquerout-ing, and delayed resolution cycles. Citizens routinely encounter portals where complaints go unacknowledged, routing decisions remain invisible, and resolution timelines stretch indefinitely, erodingpublictrustingovernanceitself.Asgovernmentsincreas-ingly deal with large volumes of structured and unstructured complaint data, the demand for intelligent automated processing has become hard to ignore. This survey examines 20 research papers from 2017 to 2026, tracing the full arc of AI-driven grievance management, from early Naive Bayes and SVM classifiers to transformer-based architectures, zero-shot LLM pipelines, Graph Neural Networks (GNNs) and blockchain-integrated multimodal frameworks. The analysis reveals a clear technological progression: statistical models established baseline classification capability but ran into scalability and generalization limits; deep learning methods like CNNsandLSTMsimprovedaccuracyatthecostofinterpretabil-ity; transformer models such as BERT and RoBERTa brought complaintunderstandingfarclosertohuman-levelperformance; and the latest graph-based and multimodal systems have further strengthened civic complaint analysis by capturing relational, spatial, and contextual signals that text-only approaches tend to miss. Key findings include the persistent challenge of multilingual support, the absence of standardized public benchmarks, the ongoing tension between interpretability and performance, and thelargelyuntappedpotentialofLLMsinzero-shotcivic NLP. This survey identifies critical research gaps and outlines directions for building grievance intelligence systems that are scalable, transparent, and grounded in fairness and inclusivity.

Introduction

Citizen grievance systems are essential platforms that connect the public with government institutions, but traditional systems face challenges due to the manual effort required for complaint reading, classification, routing, and tracking. These limitations lead to delayed responses, incorrect routing, missed urgent cases, and reduced public trust. AI-based grievance systems aim to automate and improve these processes.

The development of AI in grievance management has progressed from simple keyword matching and classical machine learning methods such as Naïve Bayes, Logistic Regression, and SVM to advanced approaches including deep learning, Transformer models (BERT), Large Language Models (LLMs), Graph Neural Networks (GNNs), and blockchain-based multimodal systems. This survey reviews 20 research papers (2017–2026) covering civic portals, educational grievance systems, social media complaints, legal chatbots, and threat detection systems.

Early machine learning systems successfully automated complaint classification but depended on small datasets and struggled with real-world generalization. Deep learning models such as CNN and LSTM improved accuracy by learning complex text patterns, but their performance depended heavily on dataset size and quality. Transformer-based models like BERT improved complaint understanding, cross-domain performance, and integration with automated workflows such as routing, escalation, and sentiment analysis.

Recent systems focus on structural and multimodal intelligence. Graph Neural Networks analyze relationships between complaints, locations, departments, and user behavior to predict escalation risks more effectively. Zero-shot LLM approaches reduce the need for labeled datasets by using pre-trained models for classification and urgency detection. Blockchain-based systems improve transparency by providing secure complaint records and accountability mechanisms.

The research categorizes grievance AI approaches into five levels:

• T1: Text-based lexical methods
• T2: Sequential learning methods such as LSTM
• T3: Transformer-based transfer learning models
• T4: Graph-based systems modeling complaint relationships
• T5: Multimodal and accountable systems combining text, images, AI, and blockchain

Comparative analysis shows that advanced models provide better accuracy and adaptability but create challenges related to transparency, computational cost, and interpretability. Classical models are easier to understand but have lower flexibility and scalability.

Major research gaps identified include:

• Lack of large, standardized, multilingual grievance datasets
• Limited transparency and explainability of AI decisions
• Poor handling of multilingual complaints and code-mixed languages
• Difficulty deploying complex AI models in low-resource environments
• Dataset bias and class imbalance issues
• Lack of common evaluation standards for comparing systems

Future research should focus on creating multilingual benchmark datasets, developing hybrid Transformer-GNN models, improving explainable AI techniques, deploying lightweight AI models, and using federated learning to improve privacy and collaboration across institutions.

Conclusion

ThissurveytracesthearcofAI-drivengrievanceredressal from early rule-based and statistical classifiers through deep learning and transformer models, to the current frontier of graph-based, zero-shot LLM, and blockchain-integrated multimodal architectures. Each stage has pushed both the per-formanceceilingandtheconceptualscopeofwhatautomation can do: early systems focused on categorizing complaints, while modern ones can predict escalation risk from relational complaintnetworks,processimageevidence,andhandletasks without any labeled data through zero-shot prompting. Three themes run consistently through this progression. First, data quality and scale tend to matter more than model complexity — systems that pair strong models with large, realisticdatasetsholdupreliably,whilethosetrainedonsmall curated corpora often overfit. Second, the tension between performance and interpretability does not go away: the most accuratemodels,GNNsandtransformersinparticular,offer limited transparency for institutional decision-making. Third, multilingual support stays largely unaddressed across the lit-erature, despite being a basic requirement for equitable and inclusive civic AI. The field is at something of an inflection point. Method-ological capabilities have matured considerably, but the sup-porting empirical infrastructure — standardized benchmarks, unifiedevaluationframeworks,real-worlddeploymentstudies has not kept pace. Closing that gap is both the central challengeandthemostconsequentialdirectionforfuturework in grievance intelligence.

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Copyright

Copyright © 2026 Navanidhi D J, Aadit Tomar, Varsha Virupaksha Gujjala, Jayanth Gowda, Basavaraj Jakkali. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.