# A Survey of Generative AI for Detecting Pedophilia Crimes
## 9 Conclusions  
Generative artificial intelligence, particularly LLMs, is emerging as a formidable ally in the fight against child exploitation. Their ability to process large amounts of information, recognize subtle patterns, and generate text that mimics human speech presents an extraordinary opportunity to automate the identification of CSAM, unmask online predators, and predict potential offenders. However, the use of generative AI in such a sensitive area requires careful attention to ethical dilemmas and potential dangers. In order to guarantee responsible and effective implementation, it is imperative to protect privacy, mitigate algorithmic bias, and prevent exploitation by nefarious actors. The deployment of generative AI for child protection must also navigate and align with evolving legal frameworks. A landmark development in this area is the EU AI Act, formally adopted in 2024, which establishes a risk-based regulatory approach. Under this legislation, AI systems intended for use in law enforcement are classified as 'high-risk', mandating that they adhere to strict requirements regarding data quality, transparency, and fundamental rights. This reinforces the necessity for robust human oversight and the ethical safeguards discussed in this review, ensuring that these powerful tools are developed and deployed responsibly [46]. To ensure their practical applicability, these AI models should be developed in collaboration with child protection organizations, cybersecurity experts, and law enforcement. Platforms that host user-generated content, such as social media networks and gaming communities, should integrate AI-driven moderation tools to detect grooming behavior while proactively adhering to strict privacy policies. Resolving ethical dilemmas, developing real-time detection and intervention frameworks, and enhancing the capabilities of generative AI systems should be top priorities for future research. We can significantly improve child protection protocols and makeprogress in the fight against pedophilic crimes in the digital age through the responsible and thoughtful use of generative AI. Future work will incorporate valuable regional studies and gray literature from non-English sources, such as Portuguese, to broaden the scope and enhance the generalizability of the findings globally. While some studies mention aspects of computational cost and efficiency, as noted in Table 5 and Section 5.4, mention aspects of computational cost and efficiency, a holistic and standardized analysis of cost-effectiveness is not consistently available across the literature. Many studies focus on accuracy metrics but omit crucial details on computational complexity, hardware demands, inference latency, and scalability costs. Therefore, a systematic evaluation of these operational factors remains a critical gap. Future research should benchmark these AI models for both their detection accuracy and their practical and financial viability for law enforcement and child protection agencies. Conceptualization, F.S., J.B. and R.R.S. ; methodology, F.S., J.B. and R.R.S. ; investigation, F.S., J.B. and R.R.S. ; writing--original draft preparation, F.S. ; writing--review and editing, J.B. and R.R.S. ; supervision, J.B. and R.R.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. No new data were created or analyzed in this study. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: