83 citations · 266 across the 39 of their papers we have counts for
17 papers · 1 filter
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…
AAAI FSS-21: Artificial Intelligence in Government and Public Sector Proceedings
Mihai Boicu, Erik Blasch, Alun Preece
Proceedings of the AAAI Fall Symposium on Artificial Intelligence in Government and Public Sector, Washington, DC, USA, November 4-6, 2021
A framework for fostering transparency in shared artificial intelligence models by increasing visibility of contributions
Iain Barclay, Harrison Taylor, Alun Preece +3
Increased adoption of artificial intelligence (AI) systems into scientific workflows will result in an increasing technical debt as the distance between the data scientists and eng…
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)…
AAAI FSS-20: Artificial Intelligence in Government and Public Sector Proceedings
Frank Stein, Alun Preece
Proceedings of the AAAI Fall Symposium on Artificial Intelligence in Government and Public Sector, Washington, DC, USA, November 13-14, 2020
A Hybrid Neuro-Symbolic Approach for Complex Event Processing
Marc Roig Vilamala, Harrison Taylor, Tianwei Xing +6
Training a model to detect patterns of interrelated events that form situations of interest can be a complex problem: such situations tend to be uncommon, and only sparse data is a…