Policy submissions

Government and parliamentary bodies and committees frequently make calls for evidence for inquiries on a range of issues. With a diverse body of expertise across various research disciplines and perspectives, The Alan Turing Institute is well positioned to submit responses to many of these calls, providing expert knowledge and advice. Below is a selection of submissions the Turing has made.

Government Office for Science & Council for Science and Technology report on computational modelling


Report summary

From the design of jet engines to new drug development and manufacture, computation modelling is becoming increasingly embedded in the design and operation of our public services, business processes, and national infrastructure. Rapid growth in the availability of data and computing power, as well as new methods for modelling complex systems, are transforming our capability in modelling.

This report, produced by the Council for Science and Technology and written by experts from academia and industry from across the UK, looks at the country’s computational modelling capability and how it could be better leveraged in both the public and private sector. The report aims to demystify computational modelling, demonstrate the UK’s capabilities, and make recommendations as to how the UK can take full advantage of the opportunities offered by advances in modelling capability.

Summary of the Turing’s involvement

Amongst the report’s recommendations, it is suggested that the government should consider whether there is a need for a centre of expertise for modelling for the public and private sectors, to promote exchange of expertise and independent critique of models. It is suggested that The Alan Turing Institute could form an important node in a networked solution to this recommendation.

The report also recommends that the Turing act as a catalyst for innovation partnerships, champion ‘grand challenges’ in modelling, and evangelise modelling’s benefits, to help policymakers and employers understand what is possible.

Sir Alan Wilson, CEO of the Turing, was one of the contributing authors of the report: “I enjoyed working with colleagues to produce this report – my main contribution was in Chapter 7 [Modelling cities and infrastructure], but it was genuinely a joint effort. As a modeller myself, I support its conclusions and will look forward to The Alan Turing Institute strengthening its offer in this field.”

Links

» Full report – “Computational modelling: technological futures”

Contributors

Sir Alan Wilson, CEO of The Alan Turing Institute

National Infrastructure Commission: Data for the Public Good


Call for Evidence

New technologies like artificial intelligence and machine learning could help cut delays and disruptions across the UK’s infrastructure network. The National Infrastructure Commission’s report examined the opportunities that these new innovations present, and made recommendations to increase open data sharing to make the most of them. A first Call for Evidence was made on 15 February 2017, with a second following on 27 July 2017.

Summary of the Turing’s submission

The recommendation is made that the government should encourage the uptake of new data-driven solutions to the asset management of critical infrastructures. Potential barriers to rollout include the differing levels of readiness and uncertainty towards integrating new methods into large-scale existing practices. The development of a national ‘digital twin’ of UK infrastructure can help to bridge geographic and sectorial divides, provide a framework for determining sensor locations, and serve as a technology demonstrator for new tools.

The submission also identifies smart traffic management as another key way to utilise data to improve our cities. By deploying open and social media data it is possible to facilitate smart urban management, repurposing existing data created by third parties and the government itself, thus creating ‘lightweight’ smart cities.

Lastly it is suggested that the effective use of big data requires greater standards to make the data accessible and usable. Currently, combining datasets from numerous sources and getting value from them is an arduous task. This would be made easier by having defined and widely accepted standards for data structures, labelling, cleanliness, security procedures, and sharing methods.

Links

» Full text of the Turing’s submission to the 1st Call for Evidence

» Full text of the Turing’s submission to the 2nd Call for Evidence

» National Infrastructure Commission: Data for the Public Good page

Contributors

Weisi Guo, Turing Fellow, University of Warwick
Mark Girolami, Programme Director for Data-Centric Engineering
Darren Grey, Programme Manager for Data-Centric Engineering
Din-Houn Lau, Department of Mathematics, Imperial College
Ricardo Silva, Turing Fellow, UCL
Victoria Stephenson, EPICentre, UCL
Tim Sullivan, Applied Mathematics, Free University of Berlin
Sebastian Vollmer, Turing Fellow, University of Warwick
Daniel Graham, Department of Civil and Environmental Engineering, Imperial College
Scott Hale, Turing Fellow, University of Oxford
Mohammed Elshafie, Department of Engineering, University of Cambridge
Julie McCann, Department of Computing, Imperial College

House of Lords Select Committee on Political Polling and Digital Media


Call for Evidence

More opinion polls than ever have been seen in recent elections, but their greater frequency has not been matched by greater accuracy. In the last seven general elections pollsters have got the result wrong three times. The committee sought to understand the impact of polls on voters and politicians, and their influence on politics and how we are governed. The role of media coverage of polls and polls carried out for interest groups, was also of interest. A Call for Evidence was made on 20 July 2017.

Summary of the Turing’s submission

The submission suggests that a possible way of improving polling methods is to encourage open access to raw poll results and population sample sizes, as well as third-party analytics. With regards to media, the suggestion is made that media analysis of polls would be more useful if it focused more strongly on quality of information and intensity of preference.

Social media naturally also has an impact on public engagement with political opinion polling, the accuracy of polling, and how seriously and ‘rationally’ we assess choices. Social media platforms such as Facebook can affect the moods of users and patterns of voter turnout, which could alter election outcomes.

However, using social media to predict elections is fraught with difficulty. The impacts of vulnerabilities in digital and social media-based polling are not well understood, and are difficult to detect and correct for. Any predictive models need to be tested through multiple elections in different countries and languages, to test their performance and generalisability. Models also can’t tell policy makers why a political shift occurred, only that it did. However, it is suggested that future advances in machine learning and natural language processing may help remedy this.

Links

» Full text of the Turing’s submission

» House of Lords Select Committee on Political Polling and Digital Media page

Contributors

Adrian Weller, Turing Fellow, University of Cambridge
Rob Procter, Turing Fellow, University of Warwick
Maria Liakata, Turing Fellow, University of Warwick
Jonathan Cave, Department of Economics, University of Warwick
Adam Tsakalidis, PhD candidate, University of Warwick

House of Lords Select Committee on Artificial Intelligence


Call for Evidence

The Select Committee on Artificial Intelligence was appointed to consider the economic, ethical and social implications of advances in artificial intelligence, and to make recommendations. The Committee wants to use this inquiry to understand what opportunities exist for society in the development and use of artificial intelligence, as well as what risks there might be. A Call for Evidence was made on 19 July 2017.

Summary of the Turing’s submission

The submission reflects the Turing’s diverse research disciplines and perspectives. It therefore covers a range of discussion points and application areas concerning AI, with a general acknowledgement that AI has the potential to transform material, social, medical, and political landscapes.

The submission identifies six crucial issues that need to be considered in determining the trustworthiness of AI systems: fairness, transparency, privacy, reliability, security, and value alignment, the combination of which depends on the context in which they are being considered.

It is recommended that government should address public perception around AI, increasing efforts to make AI technology usable through greater research into privacy, security, reliability and transparency, as well as establishing set standards.

Concern is expressed about the concentration of top AI-related talent in large companies, and the privileged access these companies have to university researchers and to individuals’ data. One proposal to tackle this issue is the creation of a data-sharing ‘patent’ system.

Lastly, caution is urged with regards to regulating AI, as specific regulation could reduce innovation and competitiveness for UK industry. Innovation is suggested as more likely to take place where government is supportive and adopts a measured approach.

Links

» Full text of the Turing’s submission

» House of Lords Select Committee on Artificial Intelligence page

» Videos of Turing representatives providing oral evidence to the committee: Video 1 | Video 2

Contributors

Adrian Weller, Turing Fellow, University of Cambridge
Ricardo Silva, Turing Fellow, UCL
Brad Love, Turing Fellow, UCL
David Barber, Turing Fellow, UCL
Theo Damoulas, Turing Fellow, University of Warwick
Maria Liakata, Turing Fellow, University of Warwick
Nathanaël Fijalkow, Turing Research Fellow, University of Warwick
Mark Briers, Programme Director for Defence and Security
Nicolas Guernion, Director of Partnerships
Helena Quinn, Policy Officer
Josh Cowls, Data Ethics Researcher

House of Commons’ Science and Technology Committee Inquiry: Life Sciences and the Industrial Strategy


Call for Evidence

The UK life sciences sector is high-tech, research-intensive, diverse and innovative. A strong life sciences sector can simultaneously benefit the UK’s economy and help improve the nation’s health. This inquiry investigated issues such as whether the Government has the necessary structures in place to support the life sciences sector; how the NHS can use procurement to stimulate innovation and the contents of the forthcoming new life sciences industrial strategy. A Call for Evidence was made on 20 July 2017.

Summary of the Turing’s submission

The response addresses questions 2 and 12 of the Call for Evidence, concerning how to stimulate innovation in the life sciences sector, and how to improve collaboration between researchers and the NHS, respectively.

For the first question, the recommendation is made that life science research outputs and publications need to be more openly shared. Open access to research data enables high quality research and insights, facilitates innovation, safeguards good research practice, increases faith in published results, and can provide a significant return on investment.

For the latter question, the recommendation is made that by leveraging the use of privacy-enhancing technologies and techniques, researchers can better collaborate with the NHS, whilst retaining the privacy of people’s health data. Such techniques can also allow joint analyses on data available from two or more branches of the NHS, or from other sources.

Further investment is also recommended into the cryptographic techniques that can allow secure analyses using cloud computing, and into harmonising metadata (information summaries about datasets) across different branches of the NHS. Such investment has the potential to transform the life sciences sector in the UK, as well as the NHS itself.

Links

» Full text of the Turing’s submission

» House of Commons’ Science and Technology Committee Inquiry page

Contributors

Kirstie Whitaker, Turing Research Fellow, University of Cambridge
Adria Gascon, Turing Research Fellow, University of Warwick
Helena Quinn, Policy Officer

House of Commons Science and Technology Committee Inquiry: Algorithms in decision-making


Call for Evidence

Algorithms are being used to make decisions in a growing range of contexts, from mortgages to sentencing criminals. How an algorithm is formulated, its scope for error or correction, the impact it may have on an individual – and their ability to understand or challenge that decision – are increasingly relevant questions. A call for evidence was made on 28 February 2017, and followed the Committee’s work on Robotics and AI, and its call for a standing Commission on Artificial Intelligence.

Summary of the Turing’s submission

The submission proposes the need for further investment for research into the technical, ethical, and legal challenges surrounding algorithms in decision-making, in which consideration of the interplay between automated and human decision-making will be crucial.

Also proposed is the scoping of an oversight institution for algorithms, e.g. an independent regulatory body to monitor and hold to account organisations which use algorithms to make decisions. It would be of the utmost importance that such a body, or bodies, that would ‘decide’ what is fair, unbiased, and transparent in the context of algorithmic decision-making, are themselves working in the best interests of the many and not the few.

Recommendation is made for the Committee to consider a ‘right to explanation’ of automated decisions, the role of human interpretation when decisions involve sensitive or personal information, and the establishment of certification mechanisms that will test algorithms for possible unethical consequences prior to their deployment.

Links

» Full text of the Turing’s submission

» House of Commons Science and Technology Committee Inquiry page

» Video of Turing representatives providing oral evidence to the committee

Contributors

Luciano Floridi, Chair of the Alan Turing Institute’s Data Ethics Group, Turing Fellow, University of Oxford
Jonathan Cave, Department of Economics, University of Warwick
Jennifer Davis, Faculty of Law, University of Cambridge
Brent Mittelstadt, Turing Research Fellow, UCL
Charles Raab, Turing Fellow, University of Edinburgh
Sandra Wachter, Turing Fellow, Oxford Internet Institute, University of Oxford
Adrian Weller, Turing Fellow, University of Cambridge
Robert Maskell, Director of High Performance Computing at Intel