83 citations · 161 across the 12 of their papers we have counts for
12 papers
The Impact of Concept Explanations and Interventions on Human-Machine Collaboration
Jack Furby, Dan Cunnington, Dave Braines +1
Deep Neural Networks (DNNs) are often considered black boxes due to their opaque decision-making processes. To reduce their opacity Concept Models (CMs), such as Concept Bottleneck…
Can we Constrain Concept Bottleneck Models to Learn Semantically Meaningful Input Features?
Jack Furby, Daniel Cunnington, Dave Braines +1
Concept Bottleneck Models (CBMs) are regarded as inherently interpretable because they first predict a set of human-defined concepts which are used to predict a task label. For inh…
Towards a Deeper Understanding of Concept Bottleneck Models Through End-to-End Explanation
Jack Furby, Daniel Cunnington, Dave Braines +1
Concept Bottleneck Models (CBMs) first map raw input(s) to a vector of human-defined concepts, before using this vector to predict a final classification. We might therefore expect…
An Experimentation Platform for Explainable Coalition Situational Understanding
Katie Barrett-Powell, Jack Furby, Liam Hiley +8
We present an experimentation platform for coalition situational understanding research that highlights capabilities in explainable artificial intelligence/machine learning (AI/ML)…
Towards human-agent knowledge fusion (HAKF) in support of distributed coalition teams
Dave Braines, Federico Cerutti, Marc Roig Vilamala +3
Future coalition operations can be substantially augmented through agile teaming between human and machine agents, but in a coalition context these agents may be unfamiliar to the…
Explainable AI for Intelligence Augmentation in Multi-Domain Operations
Alun Preece, Dave Braines, Federico Cerutti +1
Central to the concept of multi-domain operations (MDO) is the utilization of an intelligence, surveillance, and reconnaissance (ISR) network consisting of overlapping systems of r…