Artificially Intelligent systems are able to draw inferences and conclusions by analyzing information in natural language and then using such information to prove or disprove hypotheses. The quality of such inferences is directly dependent on the accuracy of the input data corpus. Given the proliferation of the Internet as well as the dubious data sources on social media, it is important to determine the truthfulness of the input information. Combining concepts of library classification, crowd-sourced curation and Google Scholar search, we propose the concept of the Veracity Index and an algorithm to calculate it. This index can be used in Artificial Systems to determine the confidence measurement of the inferences.
ENSURING VERACITY OF INPUT CORPUS FOR EFFECTIVE ARTIFICIAL INTELLIGENCE APPLICATIONS: VERACITY INDEX