Hybrid Explainable Workflows for Security and Threat Intelligence

In research into threats to safety and security, people and AI collaborate to obtain actionable intelligence. Their sources and methods often have significant uncertainties and biases. Experts are aware of these limitations, but lack the formal means to handle these uncertainties in their daily work. This project will invent a ‘metadata of uncertainty’ for threat intelligence (in both machine-readable and also human-interpretable forms) and validate it empirically. Intelligence agencies will then be able to explicitly consider the trade-off between the accuracy, proportionality, privacy, and cost-effectiveness of investigations. This will contribute towards the responsible use of AI to create a safer, more secure society.

In research into threats to safety and security, people and AI collaborate to obtain actionable intelligence. Their sources and methods often have significant uncertainties and biases. Experts are aware of these limitations, but lack the formal means to handle these uncertainties in their daily work. This project will invent a ‘metadata of uncertainty’ for threat intelligence (in both machine-readable and also human-interpretable forms) and validate it empirically. Intelligence agencies will then be able to explicitly consider the trade-off between the accuracy, proportionality, privacy, and cost-effectiveness of investigations. This will contribute towards the responsible use of AI to create a safer, more secure society.