Meta-semantic Search Engine Method Proposition for Transparent Decision Auditing

The use of search tools in decision-making and investigation processes has been gaining more and more space in the forensic community. The ability to index various sources of information and to be able to filter specific snippets and ideas is one of the milestones in the history of forensic and investigative computing. However, the widespread use of these methods, such as semantic search engines based on deep learning and machine learning methods can generate impractical results for complex cases. That's because the criteria these machines use to classify snippets of natural languages can be so complex that they're no longer auditable. Therefore, if a machine produces results that cannot be verified and explained, it is producing inferences that are highly questionable or even worth nullifying. In this work, we explore the advantages of applying data enrichment before the search process, and the subsequent use of keyword search tools to present an indexing framework with more transparent criteria and more practical results for the defense of ideas based on the findings from the use of the tools.