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Analysis of noise and bias errors in intelligence information systems
Ashraf Labib1,2, Salem Chakhar1,2, Lorraine Hope3
1Portsmouth Business School University of Portsmouth Portsmouth UK.
Summary
A new intelligence information system (IIS) improves intelligence analysis. The Team Information Decision Engine (TIDE) enhances both individual and team effectiveness and efficiency in analyzing UK Military Signals Intelligence (SIGINT) data.
Area of Science:
- Information Science
- Intelligence Analysis
- Decision Support Systems
Background:
- Intelligence analysts' judgments can be inconsistent due to noise and bias.
- Team-oriented analysis introduces further complexities.
- Existing information systems (IS) require enhancement for national security intelligence analysis.
Purpose of the Study:
- To design, implement, and validate an innovative IIS for UK Military Signals Intelligence (SIGINT) data analysis.
- To improve the effectiveness and efficiency of intelligence analysts.
- To address inconsistencies in analyst judgments through a novel tool.
Main Methods:
- Development of the Team Information Decision Engine (TIDE) tool.
- Utilizing an innovative preference learning method.
- Implementing an aggregation procedure to combine individual analyst scores.
Main Results:
- Validation trials assessed individual and team-oriented analyst performance.
- The TIDE tool demonstrated enhanced effectiveness in intelligence analysis.
- The TIDE tool showed improved efficiency for intelligence analysts at both individual and team levels.
Conclusions:
- The developed TIDE system effectively enhances intelligence analysis.
- The system improves both the effectiveness and efficiency of analysts.
- Preference learning and score aggregation are key to improving intelligence judgments.
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