## 2 Methodological Approach and Conceptual Foundations of This Position Paper
### Methodological Approach
#### 2.2.1 Methodology for the Evidence Synthesis: Systematic and Narrative Review  
The first part (1) builds on separate elements, as the literature on AI for Social Good and societal resilience is vast, and the intersections between the concepts are numerous and highly complex. Therefore, we split the review into two separate stages: the first is a systematic literature review of the concepts in the research question, and the second is a contextualization of the concepts based on a narrative exploration of the aforementioned topics in three steps. The first stage of the review provides a systematic literature review (SLR) to start the analysis and identify underexplored intersections. The SLR part aims to identify ethical concerns, governance gaps, and emerging responses that align with frameworks such as AI for Social Good and societal resilience and to refine the conceptual foundations for the framework proposed in later sections. We used the Rayyan platform (Rayyan Systematic Literature Review Tool: [[https://www.rayyan.ai/](https://www.rayyan.ai/)]([https://www.rayyan.ai/](https://www.rayyan.ai/)), access on 14 July 2025) to conduct the SLR of a series of articles from _Scopus_, _Web of Science_, _IEEE Explore_, _and ACM Digital Library_. We considered including ArXiv in the database search; however, it is mainly a pre-print repository, and we aimed to include peer-reviewed articles and chapters. Based on the initial search on Scopus for the word _sextortion_, we identified the starting point for the period of publication of the articles collected as 2017-early 2025. Prior to 2017, mentions were scarce and mostly lacked relevance to the intersection of AI ethics or blockchain infrastructure. As the analysis yields a limited number of articles for the AI-blockchain-sextortion intersection, proving the hypothesis that there is a gap in research, a supplementary SLR was deployed by expanding the search results by using the OR argument instead of AND. Moreover, for the first batch of articles, a formal quality scoring rubric was deemed less meaningful to establish thematic boundaries and identify research gaps. For the second batch used in the supplementary SLR (which adheres to PRISMA guidelines), building towards establishing the complex landscape of the topic, we used the same criteria for inclusion and focused on the period chosen. The articles were screened by each author of this position paper independently. The Boolean Logic and the inclusion/exclusion criteria are also detailed in Appendix A. The second stage of the review is the narrative exploration, which details the concepts in the systematic review to build on the conceptual landscape. If the SLR established the rarity and limitations of existing literature at the intersection of AI, blockchain, and sextortion, this section is meant to extend these findings across ethical and governance dimensions. Together, both these methods form the empirical and theoretical foundation of the proposed CF-BIAI-SXT framework presented in Section 4. This exploration is an interpretive, theory-informed process that identifies, explains, and interrelates key concepts from literature to construct a conceptual model. This approach (underlined as a valid alternative in [11]) makes sense for complex issues, multi-perspective interdisciplinary spaces, like the intersection of AI, blockchain, and sextortion--areas where there is still a lack of clarity, definitions remain fluid, conceptual frameworks are still taking shape, and the evidence base remains too limited for broad empirical generalizations. The narrative exploration conceptualizes sextortion mitigation as embedded in a multilayered ethical ecosystem that includes AI for social good, blockchain-enabled governance, societal resilience, and supporting regulatory and equity frameworks. Thus, the process not only identifies relevant themes but also explains their interconnections within the ethical landscape of AI-driven digital abuse prevention.