18 citations · 43 across the 7 of their papers we have counts for
7 papers
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…
FLAP -- A Federated Learning Framework for Attribute-based Access Control Policies
Amani Abu Jabal, Elisa Bertino, Jorge Lobo +3
Technology advances in areas such as sensors, IoT, and robotics, enable new collaborative applications (e.g., autonomous devices). A primary requirement for such collaborations is…
Federated Learning for Coalition Operations
D. Verma, S. Calo, S. Witherspoon +8
Machine Learning in coalition settings requires combining insights available from data assets and knowledge repositories distributed across multiple coalition partners. In tactical…
Using AI/ML to gain situational understanding from passive network observations
D. Verma, S. Calo
The data available in the network traffic fromany Government building contains a significant amount ofinformation. An analysis of the traffic can yield insightsand situational unde…
Quantifying Transparency of Machine Learning Systems through Analysis of Contributions
Iain Barclay, Alun Preece, Ian Taylor +1
Increased adoption and deployment of machine learning (ML) models into business, healthcare and other organisational processes, will result in a growing disconnect between the engi…
A Conceptual Architecture for Contractual Data Sharing in a Decentralised Environment
Iain Barclay, Alun Preece, Ian Taylor +1
Machine Learning systems rely on data for training, input and ongoing feedback and validation. Data in the field can come from varied sources, often anonymous or unknown to the ult…