Richard Self LLM
Senior Lecturer in Governance of Advanced and Emerging Technologies
University of Derby

Richard has taken a leading role in the field of Governance in the Department of Electronics, Computing and Maths at the University of Derby. He has used the principles of Governance in teaching Big Data, Analytics and Blockchain over the last few years. Each year, his final year students research and critically evaluate some element of new, leading edge technologies against governance principles. In 2018 they have researched and critically evaluated Blockchain Applications with some very important conclusions.
Before he worked at the University of Derby, Richard lead a range of IT related projects at Rolls-Royce Aerospace which provided an strong background in requirements’ capture and the testing of major enterprise systems.

Testing AI and Machine Learning systems using the Vs of Big Data as Guidance

As an industry, we feel that we understand the processes involved in software testing of traditional algorithmic systems, where the specifications determine exactly what the system will do, mostly in terms of business processes.

However, when we consider the self-learning systems that typify AI and Machine Learning, this is no longer sufficient. It is possible to use standard software testing approaches to verify and validate that the software meets the specifications but not that software then learns the correct and unbiased behaviour that is required.

These learning systems are defined to be able to find the patterns in the training data, whether with supervised or unsupervised training. In principle, these pattern finding systems will always find patterns in sufficiently large sets of data, many of which are entirely spurious. In addition, if the training data does not have sufficient diversity, that is it is biased, the systems will learn biased patterns.

This session will identify some of the critical factors that need to be addressed in order to mitigate some of the current problems with AI and Machine Learning systems, using the traditional 12 or 17 Vs of Big Data as guidance.

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