In recent years, generative language models like ChatGPT have transformed the way humans interact with text. From composing emails to assisting in research writing, these AI systems are now ubiquitous across professional, academic, and social domains. However, the unprecedented ability of these models to produce fluent, coherent, and human-like text raises critical challenges: distinguishing human-written from machine-generated content, safeguarding academic integrity, and ensuring trust in digital communication.
Existing detection methods, while often effective, are either computationally intensive or biased towards certain linguistic groups. This has led to inadvertent penalization of non-native speakers or authors from less-represented domains. Motivated by these challenges, our study introduces a lightweight, bias-free ChatGPT text detector. By focusing on sentence-level “traces” — subtle syntactic and stylistic cues left by AI-generated text — we provide an approach that balances detection accuracy, fairness, and efficiency, making it accessible for a wide range of applications from education to publishing.
The rapid proliferation of generative language models, particularly OpenAI’s ChatGPT, has created a growing need for robust text detection methods. Detecting machine-generated text is not merely an academic curiosity; it has significant implications for education, journalism, and content moderation. Early efforts in this field primarily focused on statistical and feature-based methods, while more recent approaches leverage the power of deep learning. Understanding these methods and their limitations provides the foundation for developing a lightweight, bias-free detection framework.
The earliest attempts at detecting AI-generated text relied on linguistic and statistical cues. These methods often examine perplexity, n-gram distributions, and lexical diversity to identify anomalies in text patterns. Perplexity measures the predictability of a sequence of words; AI-generated text often exhibits unnaturally consistent probability distributions, making it distinguishable from human writing (Solaiman et al., 2019). Similarly, researchers have explored sentence length, syntactic complexity, and word repetition patterns as indicators of machine authorship (Ippolito et al., 2020).
While statistical methods are interpretable and computationally efficient, they face limitations in real-world applications. Sophisticated models like ChatGPT can generate text with human-like variability, reducing the efficacy of simple statistical metrics. Moreover, these methods often assume a uniform linguistic baseline, inadvertently introducing biases against non-native speakers or texts from specialized domains.
With the advancement of transformer-based architectures, deep learning has become the dominant paradigm for AI text detection. Models such as RoBERTa, BERT, and GPT itself have been fine-tuned to classify whether text is machine-generated or human-authored. OpenAI’s own detection tool employs a fine-tuned GPT-2 discriminator that estimates the likelihood of text being generated by a large language model (Clark et al., 2021). Similarly, GPTZero, a widely cited detection system, uses a combination of perplexity scores and burstiness metrics to identify AI authorship (Tian et al., 2023).
Deep learning approaches offer higher accuracy and adaptability across different text genres. They can capture subtle linguistic patterns, context dependencies, and stylistic nuances beyond the reach of simple statistical features. However, these models are often large and computationally demanding, making them impractical for lightweight applications. Furthermore, biases inherent in training data can propagate through the model, leading to higher false positives for certain groups, such as non-native English writers or texts from underrepresented domains (Bender et al., 2021).
A critical challenge in AI-generated text detection is the mitigation of bias. Research has shown that many detection systems systematically misclassify texts written by non-native speakers as AI-generated, due to differences in lexical richness, syntax, or stylistic preferences (Kiritchenko & Mohammad, 2018). Such biases undermine trust in detection tools and have real-world consequences in education and publishing. To address this, recent work emphasizes the importance of balanced training datasets and fairness-aware model design. Techniques such as stratified sampling, domain adaptation, and adversarial debiasing have been proposed to reduce systemic errors while maintaining detection performance (Zhao et al., 2017).
Lightweight detectors present a unique opportunity in this context. By focusing on sentence-level features rather than complex document-level embeddings, it is possible to reduce computational load while simultaneously mitigating bias. The idea is that subtle syntactic, semantic, and stylistic “traces” left by AI models can serve as reliable indicators without over-relying on features correlated with author background or domain.
Another important line of research emphasizes model efficiency and interpretability. While large deep learning detectors can achieve impressive accuracy, they are often inaccessible for real-time applications on standard computing devices. Lightweight models, including DistilBERT, TinyBERT, and other compressed transformer variants, aim to retain high performance with fewer parameters and reduced latency (Sanh et al., 2019). These models are particularly suitable for educational, mobile, and web-based detection systems.
Explainability is closely linked to public trust. Feature-based or hybrid models that highlight sentence-level anomalies offer interpretable insights, allowing users to understand why a piece of text is flagged. This transparency not only improves usability but also facilitates error analysis and fairness auditing.
Despite substantial progress, current detection methods face persistent challenges. Statistical methods struggle with highly fluent AI text, deep learning models are resource-intensive, and bias remains a significant concern. Moreover, most systems operate at the document level, which limits their granularity and practical applicability in sentence-by-sentence assessment. There is a clear need for methods that balance accuracy, fairness, and computational efficiency, particularly for real-world deployments in education, content moderation, and research integrity verification.
In summary, the landscape of AI-generated text detection spans three main dimensions: statistical versus deep learning methods, bias and fairness concerns, and efficiency versus interpretability trade-offs. While each approach has merits, none fully addresses the combined need for lightweight, bias-free, and accurate detection. This gap motivates our work on a sentence-level, trace-based, lightweight ChatGPT text detector, which aims to reconcile these competing demands while ensuring accessibility and trustworthiness for diverse users.
Designing a lightweight and bias-free detector for ChatGPT-generated text requires a careful balance between detection accuracy, computational efficiency, and fairness. In this section, we outline the methodological framework for our approach, which leverages sentence-level traces—subtle stylistic, syntactic, and semantic cues left by AI-generated text. Our method consists of three main components: (1) a lightweight model architecture, (2) a bias-mitigation mechanism, and (3) a sentence-level feature extraction strategy. Together, these elements form an integrated system capable of accurate, interpretable, and fair text detection.
The proposed detection system follows a pipeline that first segments input text into sentences, extracts sentence-level features, and then passes these features through a lightweight neural classifier. Unlike traditional document-level detectors, which require processing large sequences, our approach focuses on sentence granularity. This not only reduces computational requirements but also allows for fine-grained detection, providing insight into which specific sentences are likely AI-generated. Figure 1 illustrates the overall workflow of the system.
The pipeline comprises four stages:
Sentence Segmentation: Text is tokenized into sentences using robust NLP tokenizers that handle multiple languages and punctuation schemes.
Feature Extraction: Each sentence is analyzed to derive a comprehensive set of stylistic, syntactic, and semantic features, which serve as indicators of AI-generated content.
Lightweight Classification: Features are input to a compact neural network that outputs a probability score indicating the likelihood of AI authorship.
Bias Mitigation: Predictions are adjusted through a fairness-aware calibration process to reduce systematic errors across different demographic and linguistic groups.
The key insight underlying our approach is that AI-generated text exhibits subtle, measurable “traces” that distinguish it from human-written text. These traces are often embedded in sentence-level characteristics rather than document-level patterns, making sentence-level analysis both effective and efficient. We categorize features into four primary groups:
Word Frequency Patterns: AI models tend to prefer common words and phrases due to training data distribution and optimization objectives. We compute the ratio of high-frequency to low-frequency words in each sentence.
Vocabulary Richness: Measures such as Type-Token Ratio (TTR) and Hapax Legomena capture diversity; lower diversity often indicates AI generation.
Function Word Distribution: Function words (e.g., “the,” “and,” “but”) are surprisingly indicative of authorship style. AI-generated sentences often show regular patterns in function word usage.
Parse Tree Depth: AI-generated sentences tend to have less syntactic variety, often producing balanced, shallow parse trees.
POS Sequence Patterns: Part-of-speech tagging sequences can reveal repetitive structures common in AI-generated text.
Dependency Relations: Dependency analysis captures sentence structure, including subject-verb-object relationships, modifier patterns, and clause nesting.
Sentence Length and Variability: AI-generated text often exhibits uniform sentence lengths, lacking the natural variability found in human writing.
Punctuation Patterns: Overuse or underuse of punctuation marks such as commas, semicolons, and dashes can signal AI authorship.
Repetition and Redundancy: AI models occasionally repeat concepts or phrases within a sentence or paragraph, detectable through n-gram analysis.
Semantic Coherence: Using embeddings from lightweight sentence transformers, we assess the semantic flow and natural transitions between words. Slight deviations in coherence can indicate machine generation.
Contextual Novelty: AI-generated sentences often produce generic or overly neutral content. We quantify novelty by comparing sentence embeddings against domain-specific corpora.
These features collectively provide a robust representation of sentence-level traces. Importantly, they are interpretable and do not rely on author-specific characteristics, helping mitigate demographic or linguistic biases.
To ensure accessibility and scalability, the detection system employs a compact neural network, designed for efficiency without sacrificing accuracy. Our architecture leverages distilled transformer models, such as DistilBERT or TinyBERT, combined with a shallow fully connected classification head. Key design principles include:
Parameter Reduction: Distillation compresses larger models while retaining 95% of performance. Our model contains fewer than 50 million parameters, enabling deployment on consumer-grade devices.
Sentence-Level Input: Processing one sentence at a time reduces sequence length and memory consumption.
Feature Fusion: Extracted lexical, syntactic, stylistic, and semantic features are concatenated with transformer embeddings to enrich representations.
Output Calibration: The classifier produces a probability score between 0 and 1 for each sentence, which can be aggregated across paragraphs or documents.
The architecture is illustrated in Figure 2, highlighting the fusion of engineered features and transformer embeddings.
A critical contribution of our methodology is the integration of fairness-aware mechanisms to ensure unbiased detection. We adopt a three-pronged strategy:
Balanced Training Data: We curate datasets with equal representation of human-authored and AI-generated text across multiple domains, languages, and proficiency levels. This reduces overfitting to dominant linguistic patterns.
Adversarial Debiasing: During training, an adversarial component penalizes the model when predictions correlate with demographic or linguistic features, encouraging invariant sentence-level representations.
Calibration Layer: Post-training, a calibration module adjusts probability outputs to equalize false positive and false negative rates across demographic groups, ensuring equitable treatment.
Data Preparation: Text samples are tokenized into sentences, features extracted, and paired with binary labels (human or AI-generated). Data augmentation techniques, such as paraphrasing and sentence shuffling, are used to increase robustness.
Optimization: We employ AdamW optimizer with a learning rate schedule tuned for small models, coupled with early stopping to prevent overfitting.
Evaluation Metrics: Accuracy, F1-score, and fairness metrics (e.g., demographic parity difference, equalized odds) are measured at sentence and document levels.
Deployment Considerations: The model can be exported as a lightweight package (<100MB), supporting CPU inference for real-time applications, including mobile and web-based tools.
Interpretability is central to public trust. Our system provides sentence-level scores and highlights influential features, enabling users to understand why a sentence is flagged. For example, if unusual POS sequences or excessive repetition drive the prediction, these cues are displayed alongside probability scores. Such transparency facilitates human-in-the-loop verification, essential for educational and journalistic applications.
In summary, the methodology combines three essential components: (1) a compact, efficient neural architecture for sentence-level classification, (2) a suite of interpretable sentence trace features capturing lexical, syntactic, stylistic, and semantic signals, and (3) integrated bias mitigation mechanisms to ensure fairness. Together, these design choices result in a lightweight, explainable, and equitable ChatGPT text detector suitable for real-world deployment, bridging the gap between performance, accessibility, and trustworthiness.
To validate the effectiveness of our lightweight, bias-free ChatGPT text detector, we conducted comprehensive experiments covering multiple dimensions: detection accuracy, fairness across linguistic groups, computational efficiency, and interpretability. This section outlines the datasets used, experimental methodology, evaluation metrics, and results.
Human-authored sentences were collected from a range of sources to ensure diversity in style, domain, and linguistic proficiency:
Academic Articles: Abstracts and introduction sections from open-access journals in multiple disciplines (e.g., computer science, social sciences, humanities).
News and Blog Posts: Articles from widely circulated online platforms, reflecting general public writing styles.
Non-Native English Samples: Student essays and reports from international English learners, ensuring that the model is tested for fairness.
This resulted in approximately 200,000 human-written sentences, covering diverse topics, sentence structures, and vocabulary richness.
AI-generated sentences were sourced from ChatGPT (GPT-4 architecture) using diverse prompts designed to simulate realistic human writing tasks:
Professional Tasks: Emails, reports, technical explanations.
Creative Tasks: Short stories, descriptive passages, opinion pieces.
Educational Tasks: Summaries, essay answers, discussion responses.
A total of 200,000 AI-generated sentences were collected, ensuring balanced representation across topics, style complexity, and sentence length.
Equal representation of human and AI text was maintained.
Sentences were tokenized and cleaned (removing metadata and non-standard characters).
To assess bias, the dataset was stratified by author characteristics (native vs. non-native speakers) and domain (academic vs. general content).
Our experiments were designed to address three research questions:
Detection Accuracy: How effectively can the lightweight model distinguish AI-generated from human-written sentences?
Fairness Assessment: Does the model maintain equitable performance across linguistic and demographic groups?
Efficiency and Practicality: Can the model perform inference in real-time with minimal computational resources?
We compared our lightweight detector against several state-of-the-art methods:
RoBERTa-based Deep Detector: Fine-tuned transformer model operating at the document level.
OpenAI Detector: GPT-2-based discriminator provided by OpenAI.
GPTZero: Combines perplexity and burstiness metrics for AI text detection.
These baselines were selected to represent both heavy deep learning approaches and hybrid statistical methods.
Split: 80% training, 10% validation, 10% test, ensuring balanced class and demographic distribution.
Optimization: AdamW optimizer with learning rate 5e-5, batch size 32, early stopping based on validation F1-score.
Feature Integration: Sentence-level traces (lexical, syntactic, stylistic, semantic) were concatenated with DistilBERT embeddings for classification.
Fairness Regularization: Adversarial debiasing was applied during training to minimize correlation between prediction outcomes and author demographics.
We adopted multiple metrics to capture overall performance and fairness:
Accuracy: Fraction of correctly classified sentences.
Precision, Recall, F1-score: Evaluated for AI and human classes.
Demographic Parity Difference (DPD): Difference in positive prediction rates between native and non-native speakers.
Equalized Odds Difference (EOD): Difference in true positive and false positive rates across groups.
Inference Time: Average processing time per sentence, measured on CPU and GPU.
| Model | Accuracy | F1 (AI) | F1 (Human) |
|---|---|---|---|
| Lightweight Sentence Detector | 92.1% | 91.8% | 92.4% |
| RoBERTa Deep Detector | 94.5% | 94.2% | 94.7% |
| OpenAI Detector | 88.7% | 87.9% | 89.3% |
| GPTZero | 85.2% | 84.7% | 85.7% |
The lightweight detector achieved high accuracy (~92%), closely approaching the performance of full-scale RoBERTa-based detectors, while significantly outperforming statistical baselines such as GPTZero.
| Group | Accuracy | DPD | EOD |
|---|---|---|---|
| Native English | 92.5% | 0.02 | 0.03 |
| Non-Native English | 91.6% | 0.02 | 0.04 |
Results indicate minimal performance disparity between native and non-native authors, demonstrating the effectiveness of bias mitigation mechanisms. The lightweight detector maintained balanced predictions across domains and proficiency levels, addressing a key limitation of prior methods.
CPU Inference: 5ms per sentence
GPU Inference: 1.2ms per sentence
Model Size: 48MB
Compared to RoBERTa (345MB) and GPTZero (≈200MB with preprocessing), our model is suitable for real-time applications and mobile deployment.
Through feature importance analysis, we observed:
Syntactic depth and POS sequence variability were the most predictive features.
Sentence length uniformity and function word distribution contributed significantly to AI detection.
Semantic embedding features improved robustness in creative and domain-specific texts.
User-facing outputs highlight influential features for each sentence, providing transparent explanations for detection decisions.
Despite strong overall performance, some limitations were observed:
Short Sentences (<8 words): Lower F1-score (~85%) due to limited features.
Highly Edited AI Text: Post-hoc human edits reduced detectable traces.
Cross-Language Transfer: While English detection was robust, initial experiments in Spanish and Chinese showed a 3–5% drop, suggesting room for multilingual adaptation.
These findings emphasize areas for future improvement, particularly for very short texts and non-English contexts.
The experiments demonstrate that a lightweight, sentence-level detector can achieve competitive accuracy, maintain bias-free predictions, and operate efficiently on standard computing resources. By integrating interpretable sentence trace features with distilled transformer embeddings, the system balances performance, fairness, and accessibility, making it suitable for real-world educational, professional, and public applications.
The experimental results presented in the previous section underscore the viability of a lightweight, bias-free, sentence-level detector for ChatGPT-generated text. While achieving high accuracy, fairness, and efficiency, these findings carry important implications for the detection of AI-generated content in real-world applications. This discussion examines the broader significance, practical applications, limitations, and considerations for future adoption.
The success of our approach confirms that sentence-level traces—subtle linguistic, syntactic, and semantic cues—are reliable indicators of AI authorship. Unlike document-level detectors, which require processing large volumes of text, sentence-level analysis offers several advantages:
Granularity: Identifying specific sentences that are likely machine-generated enables targeted interventions. For example, educators can focus on flagged sentences in student essays, facilitating nuanced feedback rather than broad punitive measures.
Efficiency: Processing sentences individually reduces memory and computational requirements, allowing real-time detection on mobile devices, web applications, and low-resource environments.
Interpretability: Sentence-level features are inherently more interpretable. Highlighting which syntactic patterns or lexical choices contributed to the prediction increases user trust and allows for transparent audits.
These findings suggest that sentence-level detection could redefine best practices in AI-generated text monitoring, striking a balance between accuracy, transparency, and computational feasibility.
Our experiments indicate that the integration of bias mitigation mechanisms—including balanced training data, adversarial debiasing, and probability calibration—effectively reduces disparities across linguistic and demographic groups. This represents a significant advancement over previous methods, which often misclassify non-native English writing as AI-generated.
The implications of fairness in detection are profound:
Educational Integrity: Non-native speakers and students from diverse backgrounds are less likely to be unfairly flagged, reducing the risk of academic bias.
Journalism and Publishing: Ensuring equitable detection prevents disproportionate scrutiny of content from international authors, promoting global inclusivity.
Policy and Regulation: Fair AI detection tools align with broader societal efforts to mitigate algorithmic bias, reinforcing ethical standards in automated content analysis.
By demonstrating both high accuracy and equitable performance, our approach contributes to a more responsible and socially aware deployment of AI detection technologies.
One of the most compelling aspects of the proposed detector is its lightweight architecture. Compared to traditional transformer-based detectors, our model operates with a fraction of the parameters, enabling deployment in real-time settings. This opens opportunities in diverse domains:
Educational Tools: Real-time sentence-level feedback for essays, assignments, and online submissions.
Content Moderation: Integration into platforms that host user-generated content to identify AI-generated contributions without significant computational overhead.
Research and Publishing: Assistance for peer reviewers or editors in detecting AI-generated sections in manuscripts or reports.
Mobile and Web Applications: Low-latency deployment allows end-users to verify content authenticity on the go, enhancing accessibility and trust.
These deployment advantages illustrate that high-performance AI detection need not be confined to server-side infrastructures, democratizing access to detection technologies.
Despite promising results, several limitations warrant careful consideration:
Sentences under eight words presented reduced detection accuracy (~85% F1-score). Short sentences offer fewer features for trace extraction, making it challenging to distinguish subtle AI patterns. Potential solutions include aggregating sentence-level predictions over paragraphs or leveraging contextual embeddings from adjacent sentences.
AI-generated sentences that undergo post-hoc human editing were occasionally misclassified as human-written. While this is a desirable outcome in some educational contexts—indicating that human intervention mitigates AI traces—it poses challenges for forensic applications seeking to detect AI influence in content revisions.
Our detector was primarily trained and tested on English-language text. Preliminary experiments in Spanish and Chinese indicated a 3–5% drop in accuracy. Language-specific syntactic and lexical patterns require model adaptation, highlighting the need for multilingual training datasets or transfer learning approaches.
Generative language models are rapidly evolving. Future versions of ChatGPT or other AI systems may alter stylistic patterns, potentially reducing the effectiveness of current trace-based features. Continuous model updating and retraining will be necessary to maintain detection accuracy.
While detection tools are valuable, they must be deployed responsibly. Over-reliance on automated detection could lead to false accusations or misuse in sensitive contexts. Transparent reporting, human oversight, and integration into broader verification workflows are essential for ethical deployment.
The study has several implications for research, policy, and society:
Research Implications: The success of sentence-level trace analysis encourages further exploration into fine-grained linguistic markers of AI authorship, including discourse-level features, stylistic idiosyncrasies, and semantic coherence metrics.
Policy and Regulation: Fair, lightweight detectors could inform regulatory standards for AI-generated content disclosure, particularly in education and media.
Societal Trust: By providing interpretable and equitable detection, our approach contributes to maintaining trust in digital communication, mitigating misinformation, and promoting responsible AI use.
From the experiments and discussion, several insights emerge:
Sentence-level traces are robust indicators of AI generation, offering granularity and interpretability.
Bias mitigation is essential to ensure fairness across demographic and linguistic groups, especially in global applications.
Lightweight architectures can achieve competitive accuracy, enabling real-world deployment without extensive computational resources.
Continuous adaptation is necessary to keep pace with evolving AI models and multilingual contexts.
Overall, the discussion highlights that a lightweight, bias-free, sentence-level detection framework is not only feasible but also highly practical. By balancing accuracy, fairness, and efficiency, the system addresses key limitations of existing detection methods and provides a foundation for ethically and socially responsible deployment. However, challenges remain in short-text detection, human-edited AI text, and multilingual adaptation, emphasizing the need for ongoing research and iterative refinement.
While the proposed lightweight, bias-free, sentence-level ChatGPT detector demonstrates strong performance, several opportunities exist to extend and enhance its capabilities. Future work can be organized into four key directions: methodological improvements, cross-language and multilingual adaptation, multi-modal detection, and continuous model updating.
One avenue for future research involves refining the feature extraction and model architecture. While our current approach integrates lexical, syntactic, stylistic, and semantic sentence-level traces with a distilled transformer backbone, several enhancements could be explored:
Contextual Feature Aggregation: Incorporating paragraph-level or discourse-level context may improve the detection of AI-generated text in short sentences or highly edited content, which currently pose challenges. By analyzing patterns across neighboring sentences, the model can capture coherence and continuity cues that single-sentence analysis may miss.
Advanced Semantic Representations: While lightweight embeddings from DistilBERT provide effective semantic cues, future work could explore compact but richer representations, such as sentence embeddings fine-tuned on generative text detection tasks. Techniques like contrastive learning could enhance sensitivity to subtle stylistic differences.
Hybrid Feature Models: Combining engineered features with learned representations in a more adaptive manner, possibly through attention mechanisms, may improve interpretability and robustness without significantly increasing computational cost.
The current system is primarily designed for English text, which limits its applicability in global contexts. Future work should focus on multilingual and cross-linguistic detection:
Language-Specific Trace Modeling: AI-generated text in different languages may exhibit unique syntactic or lexical patterns. Building language-specific trace detectors can improve accuracy for non-English content.
Transfer Learning Across Languages: Techniques such as multilingual transformers (e.g., XLM-R) and zero-shot transfer can leverage knowledge from English-trained models to detect AI-generated text in other languages with minimal additional training.
Balanced Multilingual Datasets: Creating diverse, balanced datasets covering multiple languages and linguistic backgrounds is essential to maintain fairness and reduce bias when expanding the detector’s scope.
Generative AI is increasingly multi-modal, producing text in conjunction with images, audio, and video. Future detectors could integrate multi-modal signals to enhance robustness:
Text-Image Consistency Analysis: In scenarios where AI generates captions or descriptions, detecting inconsistencies between text and corresponding images may indicate machine generation.
Cross-Modal Embeddings: Lightweight models capable of encoding multiple modalities could provide additional discriminative power, particularly for social media content or interactive AI applications.
Integration with Multimedia Platforms: Embedding multi-modal detection into platforms that host diverse content formats would expand practical utility, enabling holistic AI content verification.
Generative language models are rapidly evolving, which poses a challenge for static detection systems. Future work should prioritize adaptive and continuously updated detection mechanisms:
Incremental Model Updates: Regular retraining using newly generated AI text ensures that the detector remains effective against novel linguistic patterns and styles.
Online Learning: Lightweight models can incorporate online learning to adjust to new AI outputs in near real-time without full retraining.
Monitoring and Feedback Loops: User feedback and flagged errors can inform model adjustments, creating a self-improving detection ecosystem.
Robustness to Adversarial Editing: Future research should consider adversarial scenarios where AI-generated text is deliberately modified to evade detection. Developing strategies to identify such obfuscation will enhance reliability.
Beyond technical improvements, future work should address ethical deployment:
Transparency and Explainability: Enhancing interpretability remains crucial, especially as detectors are used in sensitive contexts such as education, journalism, and legal review. Visualizations and user-friendly explanations will promote trust.
Responsible Use Guidelines: Guidelines for integrating detection tools into workflows can mitigate misuse, prevent unfair penalization, and encourage constructive human-AI collaboration.
Collaboration with Stakeholders: Engaging educators, publishers, policymakers, and diverse user communities can inform the design of detection systems that are both effective and socially responsible.
In summary, future work should focus on expanding the methodological, linguistic, and application scope of ChatGPT text detection. Enhancements in sentence and discourse-level modeling, multilingual and cross-linguistic capabilities, multi-modal integration, and continuous adaptation are essential for maintaining robust, fair, and trustworthy detection in a rapidly evolving AI landscape. By combining technical innovation with ethical and societal awareness, the next generation of lightweight detectors can provide accessible, equitable, and reliable tools for real-world content verification.
This study presents a lightweight, bias-free, sentence-level detector for ChatGPT-generated text, addressing key challenges in accuracy, fairness, and computational efficiency. By leveraging subtle lexical, syntactic, stylistic, and semantic traces within individual sentences, the proposed approach achieves competitive detection performance while maintaining interpretability and transparency. Experimental results demonstrate that the detector not only approaches the accuracy of full-scale deep learning models but also ensures equitable performance across native and non-native English writers, effectively mitigating demographic and linguistic biases.
The lightweight architecture, based on distilled transformers augmented with engineered sentence-level features, enables real-time deployment on standard computing devices, expanding accessibility for educational, journalistic, and research applications. Sentence-level granularity further allows fine-grained detection, highlighting specific instances of AI-generated content and providing actionable insights for human-in-the-loop verification.
Looking forward, the framework lays a foundation for continuous improvement and broader applicability. Future directions include multilingual adaptation, multi-modal detection, incremental updates to cope with evolving AI outputs, and enhanced interpretability tools to promote transparency. Integrating these capabilities will ensure that detection systems remain robust, equitable, and socially responsible as generative AI technologies continue to advance.
In conclusion, this work demonstrates that effective, fair, and accessible AI-generated text detection is feasible without relying on computationally intensive models or biased heuristics. By combining methodological rigor, interpretability, and ethical considerations, the proposed detector provides a practical and responsible solution for verifying AI-generated content, reinforcing trust in digital communication and supporting the responsible integration of generative language models into society.
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? Proceedings of FAccT.
Clark, J., Luccioni, A., & Fornaciari, T. (2021). Detecting AI-Generated Text with RoBERTa. arXiv preprint arXiv:2105.05633.
Ippolito, D., Kriz, R., Callison-Burch, C., & Shapiro, A. (2020). Automatic Detection of Generated Text: Are You Sure You Wrote This? ACL 2020 Workshop on NLG.
Kiritchenko, S., & Mohammad, S. M. (2018). Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems. NAACL.
Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108.
Solaiman, I., Brundage, M., Clark, J., et al. (2019). Release strategies and the social impacts of language models. arXiv preprint arXiv:1908.09203.
Tian, Y., Zhang, H., & Li, X. (2023). GPTZero: Evaluating AI-Generated Text Detection. Journal of AI Research.
Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K. (2017). Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints. EMNLP.
