Funder: Defence Science and Technology Laboratory (Dstl), UK
Date: 2019
Funding: £80,000
Partner: MASS Ltd
In this project, we use the human-machine teaming approach to design a new visual analytics system that supports kill chain elicitation:
- The system allows user to mark up text in intelligence reports that may form part of a kill chain;
- The system uses machine learning to learn from user annotation and recommend new information that may be part of a kill chain;
- The analyst then comments on the recommendations indicating which is relevant and which is not;
- The machine learning model uses the feedback to improve its model, which lead to better recommendation;
- This process is iterative, so the system keeps improving as the user provides more feedback (this is also known as ‘active learning’);
- The system can also provide explanation of why recommendation is made (‘explainable AI’), and the analyst can directly comment the explanation to instruct the model how to improve.
Human-AI Teaming 