The Alan Turing Institute is the national institute for data science and artificial intelligence, with headquarters at the British Library.
The Institute is named in honour of Alan Turing whose pioneering work in theoretical and applied mathematics, engineering and computing are considered to be the key disciplines comprising the fields of data science and artificial intelligence.
Koorosh Aslansefat, who has recently made a breakthrough in his particular field of study, said: “I am delighted to have won this award and to be contributing to making technology and life safer in the future.
"I would like to thank my supervisor Professor Yiannis Papadopoulos for his continuous support."
The Dependable Intelligent Systems research group at the University of Hull is internationally renowned for pioneering technologies that make technology more dependable.
Professor Yiannis Papadopoulos, who supervises this research group said: “For Koorosh to be recognised in this way by the Alan Turing Institute is a testament to his innovative approach and potential to advance breakthroughs on improving the safety of Artificial Intelligence. I would like to congratulate Koorosh on his achievements and the impressive contribution he makes to our research at the University of Hull.”
Explaining the work of the Dependable Intelligent Systems research group, Professor Papadopoulos said: “Many experts predicting the technological future of humanity claim that Artificial Intelligence poses existential risks for humanity. In the extreme, super-intelligent machines could threaten humanity or the stability of the economy. In simpler scenarios, autonomous cars may fail to correctly read traffic lights or traffic signs in adverse environmental conditions, and systems that rely on face recognition could discriminate against individuals or groups.
Koorosh has recently made a breakthrough in this area developing a technology called Safety of Machine Learning (SafeML).
Machine learning algorithms can learn by analysing large volumes of data. For example, an algorithm may learn to detect a disease by examining images of typical symptoms of this disease.
The accuracy of the reasoning of the algorithm depends on these data. Thus, the algorithm may be biased towards these typical symptoms and may give false negatives when encountering atypical symptoms of the disease.
In another scenario, an algorithm may have been trained to detect features on a human face by examining pictures of a certain ethnic group. It may fail to detect similar features when it encounters images of people of different ethnic origins.
SafeML uses statical techniques to detect and report on such biases. This makes machine learning more accurate, just and safe which is a significant contribution to technology and humanity.
This project is a part of the EU Project SESAME (Secure and Safe Multi-Robot Systems 2021-2024)) in which the University of Hull plays a central role in terms of technological contribution.”
The purpose of the Alan Turing Institute Award for Postdoctoral Enrichment Award is to facilitate growth of a community of researchers with a shared interest in data science and AI throughout the UK.
The award provides the winner with the opportunity to participate in the Institute’s online community for researchers in data science and access to training and research showcase events for the Turing community, such as the AI UK 2022 conference.
The Institute undertakes research which tackles some of the biggest challenges in science, society and the economy. It collaborates with universities, businesses and public and third sector organisations to apply this research to real-world problems, with lasting effects for science, the economy, and the world we live in.