All Outputs (4)

Identifying Roles, Requirements and Responsibilities in Trustworthy AI Systems (2021)
Conference Proceeding
Barclay, I., & Abramson, W. (2021). Identifying Roles, Requirements and Responsibilities in Trustworthy AI Systems. In UbiComp '21: Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers (264-271). https://doi.org/10.1145/3460418.3479344

Artificial Intelligence (AI) systems are being deployed around the globe in critical fields such as healthcare and education. In some cases, expert practitioners in these domains are being tasked with introducing or using such systems, but have littl... Read More about Identifying Roles, Requirements and Responsibilities in Trustworthy AI Systems.

PyDentity: A playground for education and experimentation with the hyperledger verifiable information exchange platform (2021)
Journal Article
Abramson, W., Papadopoulos, P., Pitropakis, N., & Buchanan, W. J. (2021). PyDentity: A playground for education and experimentation with the hyperledger verifiable information exchange platform. Software Impacts, 9, Article 100101. https://doi.org/10.1016/j.simpa.2021.100101

PyDentity lowers the entry barrier for parties interested in experimenting with the Hyperledger’s verifiable information exchange platform. It enables educators, developers and researchers to configure and initialise a set of actors easily as associa... Read More about PyDentity: A playground for education and experimentation with the hyperledger verifiable information exchange platform.

Evaluating Trust Assurance in Indy-Based Identity Networks Using Public Ledger Data (2021)
Journal Article
Abramson, W., Hickman, N., & Spencer, N. (2021). Evaluating Trust Assurance in Indy-Based Identity Networks Using Public Ledger Data. Frontiers in Block Chain, 4, Article 622090. https://doi.org/10.3389/fbloc.2021.622090

Privacy and Trust Redefined in Federated Machine Learning (2021)
Journal Article
Papadopoulos, P., Abramson, W., Hall, A. J., Pitropakis, N., & Buchanan, W. J. (2021). Privacy and Trust Redefined in Federated Machine Learning. Machine Learning and Knowledge Extraction, 3(2), 333-356. https://doi.org/10.3390/make3020017

A common privacy issue in traditional machine learning is that data needs to be disclosed for the training procedures. In situations with highly sensitive data such as healthcare records, accessing this information is challenging and often prohibited... Read More about Privacy and Trust Redefined in Federated Machine Learning.