Information ergonomics and cognitive dissonance by AI in HUMINT/OSINT processes

The study explores the balance between enhancing situational awareness and maintaining good information ergonomics in AI-supported HUMINT/OSINT processes. The proposition for the experimental research was the leveraging effect of organizing and filtering as well as recognition algorithms in HUMINT and OSINT. Increased effectiveness of the due to less cognitive load and better fit to information processing. Especially repetitive activities as well as maintaining attention on several instances of critical events call for robust and explainable methods for information processing. The key issue is maintaining situational awareness on level of intelligence tasks as well as on the meta-level, i.e. organizing intelligence tasks. Simple algorithms and AI powered methods can enhance situational awareness in time-critical operations, but they may also cause cognitive dissonance as operators question the accuracy of the AI-provided information, leading to additional cognitive load and poor information ergonomic state. The results are based on constructive research process. Methods were designed by operators yet put into action by external developers. Experimental phase consisted of reanalysis of intelligence data and information. Validation in this setting is based of expert assessment, evidence on good functionality, and effect on information ergonomics. Acceptance and trust in AI are crucial to avoid cognitive dissonance and increased cognitive load and those factors are also discussed in the paper.