Nick Ryman-Tubb

University of Surrey · The Surrey Business School

Scholars often recommend incorporating context into the design of an explainable artificial intelligence (XAI) model in order to ensure successful real-world adoption. However, contemporary literature has so far failed to delve into the detail of what constitutes context. This paper addresses that gap by firstly providing normative and XAI-specific...

This paper offers preliminary reflections on the sustainability ten- sions present in Artificial Intelligence (AI) and suggests that Para- dox Theory, an approach borrowed from the strategic management literature, may help guide scholars towards innovative solutions. The benefits of AI to our society are well documented. Yet those benefits come at...

The core goal of this paper is to identify guidance on how the research community can better transition their research into payment card fraud detection towards a transformation away from the current unacceptable levels of payment card fraud. Payment card fraud is a serious and long-term threat to society (Ryman-Tubb & d’Avila Garcez, 2010) with an...

A set of payment card transactions including a sparse set of fraudulent transactions is normalized, such that continuously valued literals in each of the set of transactions are transformed to discrete literals. The normalized transactions are used to train a classifier, such as a neural network, such that the classifier is trained to classify tran...

Neural networks have represented a serious barrier-to-entry in their application in automated fraud detection due to their black box and often proprietary nature which is overcome here by combining them with symbolic rule extraction. A Sparse Oracle-based Adaptive Rule extraction algorithm is used to produce comprehensible rules from a neural netwo...

This paper presents a novel approach to knowledge extraction from large-scale datasets using a neural network when applied to the real-world problem of payment card fraud detection. Fraud is a serious and long term threat to a peaceful and democratic society. We present SOAR (Sparse Oracle-based Adaptive Rule) extraction, a practical approach to pr...

Neural networks are mathematical models, inspired by biological processes in the human brain and are able to give computers more "human-like" abilities. Perhaps by examining the way in which the biological brain operates, at both the large-scale and the lower level anatomical level, approaches can be devised that can embody some of these remarkable...

Neural networks are mathematical models, inspired by biological processes in the human brain and are able to give computers more “human-like” abilities. Perhaps by examining the way in which the biological brain operates, at both the large-scale and the lower level anatomical level, approaches can be devised that can embody some of these remarkable...

They all stink: food and drink, perfumery, household products, soaps, shampoos, paints, manufacturing processes, printing processes, waste products, contaminated air, and automotive emissions and environmental testing. In every case, small is a criterion of quality. Automated techniques to ‘smell’ or ‘taste’ liquids using mass spectrometry and gas...

Gone are the days of computers which could only do what they were told to by humans. The neural computer has changed all that Neural means "of the nervous system", and the biological comparison is no coincidence. Just like the human brain, neural computers can learn from experience and apply their knowledge.

The rules that govern many of the processes within the manufacturing industry can be hard to define and even harder to write down. Nick Ryman-Tubb explains how neural network technology can help.