Reliability: understanding cognitive human bias in artificial intelligence for national security and intelligence analysis

Technology advances neither enhances nor detracts from cognitive human bias since its existence over thousands of years. However, skewness in conventional artificial intelligence mechanisms leads to algorithmic mishaps. Understanding the interconnection between human and machine, that algorithms are subject to bias through interactions, and the output of algorithmic bias forms a source of inspiration to the U.S. Intelligence Community (IC). As a result of emerging technologies such as artificial intelligence (AI) and its subset of machine learning, complex cybercrimes raises security concerns and highlights pertinent considerations for national security; infectious diseases can be a national security concern and facilitate bioterrorism and the production of biological weapons; and automation systems in the form of unmanned aerial systems (UAS) can have national security implications as an attractive tool for criminals and nefarious actors. This article uses grounded theory to analyse congressional hearing reports and related official documents. Increasing our understanding of the process leads to output attainment since AI operates at the speed of light and more data than we can humanly manage accessible. The present research intends to contribute to international relations, international studies, security and strategic studies, and science and technology studies.