Special Conference Tracks: 
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Trading Data for Health – Balancing Ethics, Economics and Technology
Track Chair: Anil Bharath, Imperial College London
There is effort and investment in using personal data for healthcare. Such data includes that which we  knowingly capture and make use of ourselves – such as from wearable sensors and “activity monitors” – to medical records, involving measurements that we might consider very sensitive.  What are the benefits that harvesting the full spectrum of medical data, from images through to genetic profiles, bring to healthcare? And how do we ensure that this benefit – if delivered through commercial activities – recognises the contribution of the individual? This session will include content on technology for data management and sharing, economic models around machine learning from healthcare data, and open questions on policy.
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Blockchain & Data Governance
Track Chair: Catherine Mulligan, World Economic Forum, UN Digital Cooperation, Imperial College, UCL
A relatively new digital currency emerged in 2008 called Bitcoin. Having now moved far beyond it’s original use in the creation of cryptocurrencies, blockchains are now being proposed as solutions to nearly all of the world’s problems where ‘trust’ is seen as an issue – more often than not, these solutions involve the use of blockchain for data security, integrity and so-called ‘proveability’. This track investigates the role that blockchain may play in the emerging field of Data Governance in recognition that data and the way it is managed is moving beyond merely a technology space and moving into one that is deeply affected by social norms, political economy, regulation and policy. Topics include, but are not limited to: Data as an asset, the political economy of data governance, distributed-system solutions for data governance (both blockchain and non-blockchain based) of privacy etc… , defining of research agendas in this space.
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Data Practices, Lessons and Challenges: A Private-Sector (Business) Perspective
Track Chair: Bilal Gokpinar, University College London
Data is the next frontier in innovation, productivity, and competition for businesses. Data and digitisation affect business processes, supply chain operations, consumer, industry and market dynamics. It threatens established business models, and introduces new ways for companies to create and capture value. In addition to data generated by traditional transaction-based enterprise systems, companies increasingly capture large volumes of structured and unstructured data about their customers and operations through social media sites, mobile phones, consumer devices, as well as networked sensors, smart machines and others.

We invite submissions to explore this emerging multifaceted phenomenon with a focus on private sector organisations. How does the nature of businesses and business operations change in the age of digitisation and data? What kind of opportunities and challenges do data-rich environments pose to small and large enterprises? How can companies create and capture value, and enhance their productivity and competitiveness through data and analytics? How can private sector organisations get closer to various stakeholders (e.g., consumers, suppliers, governments) with data but at the same time manage corresponding risks appropriately?

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Successful Uses of Data Science and AI for Public Good
Track Chair: Tom Smith, Office for National Statistics (ONS Data Science Campus)
Data Science and its application into Artificial Intelligence (AI) provide new and previously unimagined opportunities for public services and policy-makers to take advantage of the dizzying array of novel data sources and exponential increases in computer processing power.
This conference stream/track will look at how Data Science and Artificial Intelligence are being used across public policy different domains to enhance public policies that impact on people, places and the economy. Examples will include (i) research projects that have provided better analysis and/or new insights to support decision makers; (ii) examples of improved operations or automation; and (iii) better statistics and data sets that have helped to set and evaluate public policy.
The session will include presentations from across the UK public sector that give practical examples of how Data Science and AI are helping to inform and implement public policies and services in the UK and beyond.
The examples could include more efficient and effective policy implementation, delivery or evaluation; producing enhanced or new statistics, real-time indicators or more granular policy insights provided through the use of machine learning and other data science methods applied to processing unstructured text, image recognition or the vast data sets produced by different types of sensors or large-scale administrative records.
As well as the benefits delivered through Data Science and AI, presenters will be invited to consider the technical, ethical and practical challenges of working in public policy and the lessons learned for the future.
The stream will seek to include policy-makers’ perspectives and questions around the quality assessment of the evidence base emanating from Data Science approaches, the appropriateness of AI based solutions to operational delivery and the need for regulatory and/or assurance frameworks.