2 citations · 3 across the 5 of their papers we have counts for
5 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…
Analysing Explanation-Related Interactions in Collaborative Perception-Cognition-Communication-Action
Marc Roig Vilamala, Jack Furby, Julian de Gortari Briseno +3
Effective communication is essential in collaborative tasks, so AI-equipped robots working alongside humans need to be able to explain their behaviour in order to cooperate effecti…
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)…