Uncertainty-Aware Artificial Intelligence and Machine Learning

Watch the video

Military / Coalition Issue

Military coalitions must operate in dynamic and contested environments with constrained sharing policies leading to limited data to adapt AI systems. This leads to the possibility of high epistemic uncertainty that causes AI to makes poor recommendation potentially leading to disastrous decision-making.

Core idea and key achievements

Developed methods to extract the epistemic uncertainty inherent in neuro-symbolic AI&ML systems trained with limited data. At the symbolic layer exact inference algorithms have been modified to percolate second-order probabilities to enable the answering of queries with confidence bounds.

At the neural layer, evidential deep learning (EDL) is specially trained to characterize the amount of relevant evidence for the various alternatives in light of the input (sensor) data and the data to train the AI network. In many different applications, it is demonstrated that EDL can detect out-of-distribution test samples. Furthermore, accuracy increases when deferring decision-making on highly uncertain test data.

image info

Implications for Defence

The EDL framework can be applied to numerous target classification systems. It allows such a systems to alert decision makers when it no longer is able to provide reliable recommendations, which is possibly due to changes in the operational environment relative to how the system was trained.

image info

This enables decision makers to rely on the system only when it is reliable.

image info

Readiness & alternative Defence uses

Provides a framework for training deep learning systems to be uncertainty-aware. Presented at UK Defence’s AI Fest 3 and concepts embraced by AI research & development community. Initial methods have been tested on simulated and academic data sets. Work is needed to develop relevant military data sets for evaluation and advancement of the training framework and inference methods that enable uncertainty-aware AI & ML.

Resources and references


Cardiff University, ARL