# A Systematic Review of Responsible Artificial Intelligence Principles and Practice
## 3 Finding 1: Topics and Themes in Review Results  
The final corpus was subject to a deep analysis and synthesis of concepts, relationships, summary findings and conclusions in order to develop a broad appreciation of the current state of responsible AI principles and practice. To systematically analyze the content of the selected publications, semantic grouping based on content similarity was applied. BERTopic [44], a state-of-the-art topic modeling technique that combines transformer-based embeddings with traditional clustering approaches, was employed. The BERTopic pipeline consists of several key steps: first, documents are embedded using OpenAI's text-embedding-3-large model, followed by dimensionality reduction using Uniform Manifold Approximation and Projection (UMAP) [45] to prepare the data for clustering. The reduced embeddings are then clustered using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) [46], a density-based clustering algorithm that can identify clusters of varying shapes and sizes while effectively handling outliers. BERTopic generated 40 initial distinct topics. These topics were derived by combining titles, abstracts, and keywords from each publication into a unified text corpus. The documents within each group were then processed using a class-based TF-IDF transformation to identify the most representative terms. Through a systematic consolidation process based on four techniques (semantic similarity, thematic overlap, topic frequency, and coherence of grouped topics), 40 initial distinct topic clusters were identified. These 40 clusters were refined into 15 major themes. Of the initial 553 articles, BERTopic classified 60 as outliers (11%). One reviewer went through all the outliers after the thematic analysis and assigned 21 documents to relevant topics. This resulted in a final analyzed corpus of 514 (94%) articles. These outlier documents either represented intersections between multiple topics or did not align clearly with any of the identified themes, indicating potential emerging or cross-cutting research areas in the responsible AI domain. Once the initial topic analysis was done, three independent runs were again executed with different random initialization seeds. Each run produced between 38 and 46 initial clusters, these were further evaluated to determine the presence of the same 15 major themes. The visualization of topic embeddings in two-dimensional space, as per Figure 2, reveals patterns in the responsible AI research landscape. The 15 identified topics show distinct positioning that illustrates their relationships and boundaries. Domain-specific applications of responsible AI principles appear toward the periphery of the embedding space. Topic 0 (AI in Healthcare and Digital Medicine) occupies a distinct region due to its specialized vocabulary and unique regulatory considerations. Topic 2 (ChatGPT and Academic Integrity in Education) forms a separate cluster with its focus on educational contexts. Topics 5 (Generative AI, Creativity, and Intellectual Property) and 4 (AI-driven Finance, Regulation, and Corporate Accountability) maintain peripheral positions with their specialized terminology and regulatory frameworks. Topic 9 (Sustainable AI for Agriculture and Environment) combines environmental concepts with technical approaches, creating a distinct profile. Topic 14 (Federated and Privacy Preserving AI) forms a defined cluster due to its technical methodological focus. The central region contains topics related to fundamental responsible AI principles in a nuanced arrangement. Topic 7 (Explainable and Interpretable AI) and Topic 13 (Designing for Trust and Trustworthiness in AI) appear in close proximity, suggesting a relationship between explainability and trust in AI systems. Topic 6 (Moral Agency, Accountability, and AI) forms its own region, representing philosophical foundations of machine ethics, while Topic 1 (Responsible AI Principles and Stakeholder Governance), a broad research area, occupies a middle position.