The Royal Society comments on the Government’s response to the Science and Technology Select Committee’s report on Robotics and Artificial Intelligence

Professor Ottoline Leyser FRS and Professor Genevra Richardson FBA, Co-chairs of the Royal Society and British Academy data governance project, said:

“We welcome the government’s signal that it will draw on expertise to establish the best principles and the soundest regulation to guide the development and application of technologies like AI and machine learning.

“Data-driven technology is all around us, in our phones, in the smart meters in our homes, in toys and gadgets, and other smart devices. These technologies offer many benefits which could transform our society including improving diagnostics in healthcare, creating more efficient transport and energy systems, and a step change in manufacturing capabilities. However, where technologies are analysing huge amounts of potentially sensitive data there is also inherent risk, which must be safeguarded against.

“In the UK we have world class expertise in data science, ethics and a rapidly growing tech sector. There is an opportunity for the UK to lead internationally in guiding new data sciences. At the Royal Society and British Academy we are drawing on this expertise and connecting debates across different sectors to identify the best response to the ethical and social impacts of these new technologies. We hope our recommendations will inform the development of the best data governance framework, to manage risks to individuals and communities and maximise the major social and economic benefits for all of society.”

Adds Peter Donnelly, chair of the Royal Society’s Machine Learning working group:
“Machine learning is already being used in a range of applications that we interact with every day, and has the potential to drive further innovations that could improve public services and create significant economic value. The Royal Society’s machine learning project is investigating the potential of machine learning over the next 5-10 years, and the barriers to realising that potential. We look forward to further contributing to the development of machine learning in the UK as the project continues, and will be publishing our findings shortly”.