Salem Chakhar
CREST Outputs
Projects
Academic Publications
Analysis of noise and bias errors in intelligence information systems
An intelligence information system (IIS) is a particular kind of information systems (IS) devoted to the analysis of intelligence relevant to national security. Professional and military intelligence analysts play a key role in this, but their judgments can be inconsistent, mainly due to noise and bias. The team-oriented aspects of the intelligence analysis process complicates the situation further. To enable analysts to achieve better judgments, the authors designed, implemented, and validated an innovative IIS for analyzing UK Military Signals Intelligence (SIGINT) data. The developed tool, the Team Information Decision Engine (TIDE), relies on an innovative preference learning method along with an aggregation procedure that permits combining scores by individual analysts into aggregated scores. This paper reports on a series of validation trials in which the performance of individual and team-oriented analysts was accessed with respect to their effectiveness and efficiency. Results show that the use of the developed tool enhanced the effectiveness and efficiency of intelligence analysis process at both individual and team levels.
(From the journal abstract)
Labib, A., Chakhar, S., Hope, L., Shimell, J., & Malinowski, M. (2022). Analysis of noise and bias errors in intelligence information systems. Journal of the Association for Information Science and Technology, 73(12), 1755–1775. https://doi.org/10.1002/asi.24707
https://asistdl.onlinelibrary.wiley.com/doi/10.1002/asi.24707
Projects
Academic Publications
Analysis of noise and bias errors in intelligence information systems
An intelligence information system (IIS) is a particular kind of information systems (IS) devoted to the analysis of intelligence relevant to national security. Professional and military intelligence analysts play a key role in this, but their judgments can be inconsistent, mainly due to noise and bias. The team-oriented aspects of the intelligence analysis process complicates the situation further. To enable analysts to achieve better judgments, the authors designed, implemented, and validated an innovative IIS for analyzing UK Military Signals Intelligence (SIGINT) data. The developed tool, the Team Information Decision Engine (TIDE), relies on an innovative preference learning method along with an aggregation procedure that permits combining scores by individual analysts into aggregated scores. This paper reports on a series of validation trials in which the performance of individual and team-oriented analysts was accessed with respect to their effectiveness and efficiency. Results show that the use of the developed tool enhanced the effectiveness and efficiency of intelligence analysis process at both individual and team levels.
(From the journal abstract)
Labib, A., Chakhar, S., Hope, L., Shimell, J., & Malinowski, M. (2022). Analysis of noise and bias errors in intelligence information systems. Journal of the Association for Information Science and Technology, 73(12), 1755–1775. https://doi.org/10.1002/asi.24707