# A Survey of Generative AI for Detecting Pedophilia Crimes
## 4 Literature Review: AI Approaches for Detection
### Comparative Analysis
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**Category** & **Limitation** & **Studies Citing This Limitation** \\ \hline \multirow{8}{*}{Data-Related} & Data Scarcity, Quality and Imbalance: Lack of large, high-quality, labeled, and balanced datasets. [PERSON] et al. (2018) [17], [PERSON] et al. (18), [PERSON] et al. (19), [PERSON] et al. (2028), [PERSON] et al. (30), [PERSON] et al. (31) \\  & Outdated Datasets: Reliance on older datasets (e.g., PAN2012) that may not reflect current online behaviors. [PERSON] et al. (33) \\  & Lack of Realism: Datasets using volunteers instead of real victims can lack authenticity. & [PERSON] et al. (27), [PERSON] et al. (33) \\  & Noisy and Unreliable Data: User-generated content is often unstructured, contains misspellings, and slang. & [PERSON] et al. (26), [PERSON] et al. (21) \\ \hline \multirow{8}{*}{Technical/Methodological} & Model Reliability and Consistency: LLMs can provide inconsistent or overly cautious responses and are prone to “hallucination”. & \multirow{2}{*}{[PERSON] and [PERSON] (11)} \\  & Contextual Understanding: Difficulty in interpreting nuanced, figurative, or evolving language. & [PERSON] et al. (7), [PERSON] et al. (29), [PERSON] et al. (32) \\ \cline{1-1}  & Difficulty of the Task: The inherent complexity of distinguishing legitimate content from harmful content. & [PERSON] et al. (16), [PERSON] et al. (26) \\ \cline{1-1}  & Recall Bias and Methodological Rigidity: Limitations in non-AI studies, such as recall bias in surveys or rigid window sizes. & [PERSON] et al. (9), [PERSON] et al. (25) \\ \hline \multirow{8}{*}{Ethical and Privacy} & Privacy Concerns: Monitoring online communications raises significant privacy issues. & [PERSON] et al. (7) \\ \cline{1-1}  & Algorithmic Bias: Models can have inherent biases (e.g., from BERT) that need mitigation. & [PERSON] et al. (24) \\ \cline{1-1}  & Data Sensitivity: Handling real reports from abuse hotlines requires extreme care and restricts data availability. & [PERSON] et al. (6) \\ \hline \multirow{8}{*}{Implementation and Generalization} & Limited Generalizability: Difficulty in applying models trained on one dataset to different, real-world scenarios. [PERSON] et al. (14), [PERSON] et al. (18) \\ \cline{1-1}  & Computational Cost: The cost of training and deploying large or federated models can be a significant barrier. [PERSON] et al. (24) \\ \cline{1-1} \cline{2-3}  & \multicolumn{1}{p{113.8 pt}}{} & \multicolumn{1}{p{113.8 pt}}{} & \multicolumn{1}{p{113.8 pt}}{} \\ \end{tabular}
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Table 4: Categorization of Identified Limitations. This table organizes the challenges and limitations reported in the surveyed studies into four distinct categories: Data-Related, Technical/Methodological, Ethical and Privacy, and Implementation and Generalization, providing example studies for each described limitation. \begin{table}
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Table 3: _Cont._The surveyed AI-based methods can be classified by their intervention timeline, which determines whether they act retrospectively on existing data or proactively to prevent future harm. This distinction is crucial for understanding their practical application by law enforcement and child protection agencies (Table 5). After exploring the state of the art in traditional ML and emerging GenAI techniques for detecting grooming behavior, the following section will highlight the benefits that these new models bring to this complex domain.