Data for Policy 2019

4th International Conference

Data for Policy 2019:Digital Trust and Personal Data

11-12 June 2019, London

Data science technologies, pioneered in the private sector, are now ripe for transforming the public sector. However, both government policy and technology providers need to address two pressing public concerns: DIGITAL TRUST (privacy and security) and PERSONAL DATA (ownership and beneficial exploitation).

The impact from ‘smartification’ of public infrastructure and services will be far more significant in comparison to any other sector given the government’s function and importance to every individual and institution. Potential applications range from public engagement through natural text and speech Chatbots, to providing decision support for civil servants via AI-based Robo-advisors, to real-time management of the public infrastructure through the Internet of Things and blockchain, to securing public records using distributed ledgers, and, encoding and codifying laws using smart contracts.  However, in many cases current uses of automated decision-making systems have been shown to cause adverse impacts on important life events of individuals – examples range from bias in recruitment of job-applicants, to credit scoring in loans and insurance, and to sentencing of criminals. Also, state surveillance and manipulation of voter behaviour have become the early examples of how such developments may amplify the asymmetry of power (between citizen and those utilising such technologies) causing severe damage to the democratic processes. The Bitcoin ‘hype’, with its correlating energy usage, has also shown the environmental cost of the highly complex computations, as well as indicating other potential unpredicted and unintended consequences.  On the other hand, the cost of not using – or the slow uptake of – data science technologies in the public sector is also potentially huge, given that all other aspects of our lives are changing fast under the ongoing digital revolution. It then follows that the stakes could be much higher in both the use and the avoidance of these technologies for public decision making and service delivery. This will require a careful cost/benefit analysis before implementation at scale.

The fourth conference in the Data for Policy series therefore highlights ‘Digital Trust and Personal Data’ as its main theme. The conference will also welcome contributions in the broader data science for government and policy discussions. In particular, submissions around the value and harm of using data in the public sector, deployment experience in government, ‘digital ethics’ and ‘ethics engineering’ concepts, personal data sharing frameworks and technologies, transparency in machine learning processes, analytics at source, and secure data transaction methodologies are encouraged.

Topics invited include but are not limited to the following:

  • Data, Government and Policy: Digital era governance and democracy, data and politics, asymmetry of power, data- and evidence-driven public service delivery, algorithmic government and regulation, open-source and open-data movements, multinational companies and privatization of public services, sharing economy and peer-to-peer services, online communities, crowdsourcing, citizen science, public opinion, data literacy, policy laboratories, case studies and best practices.
  • Technologies: Artificial Intelligence, Big Data, blockchain distributed ledger and smart contract technologies, behavioural and predictive analytics, the Internet of Things, platforms, Global Positioning Systems (GPS), biometric identifiers, augmented and virtual reality, robotics, and other relevant technologies.
  • Systems & Infrastructure: Data collection, capture, storage, processing and visualisation technologies; platforms and web services, mobile applications, meta-data, standards and interoperability, databases and data warehousing, high performance computing, algorithms, programming, decision support systems, user-interaction technologies, and other relevant topics.
  • Data Processing & Knowledge Generation:Data representation and pre-processing, integration, real-time and historical data analysis, mathematical and statistical models, ‘data-driven’ analysis, human-in-the-loop (HITL); mixed methodologies, secondary data analysis, web mining; Randomised Controlled Trials (RCTs), gaps in theory and practice, other relevant topics.
  • Policy for Data & Management: Data governance and regulatory frameworks; General Data Protection Regulation (GDPR); data collection, storage, curation and access; data security, ownership, linkage; data provenance and expiration; private/public sector/non-profit collaboration and partnership;capacity-building and knowledge sharing within government; institutional forms and regulatory tools for data governance.
  • Privacy, Security, Ethics & Law: Ethical concerns around data, algorithms, and interactions (both human-machine and machine-machine interactions) and associated technology responses; legal status of digital systems; bias, transparency and accountability of digital systems; public rights, free speech, dialogue and trust.

The fourth conference in the Data for Policy series therefore highlights ‘Digital Trust and Personal Data’ as its main theme. The conference will also welcome contributions in the broader data science for government and policy discussions.  In particular, submissions around the value and harm of using data in the public sector, deployment experience in government, ‘digital ethics’ and ‘ethics engineering’ concepts, personal data sharing frameworks and technologies, transparency in machine learning processes, analytics at source, and secure data transaction methodologies are encouraged.

Topics invited include but are not limited to the following:

  • Data, Government and Policy: Digital era governance and democracy, data and politics, asymmetry of power, data- and evidence-driven public service delivery, algorithmic government and regulation, open-source and open-data movements, multinational companies and privatization of public services, sharing economy and peer-to-peer services, online communities, crowdsourcing, citizen science, public opinion, data literacy, policy laboratories, case studies and best practices.
  • Technologies: Artificial Intelligence, Big Data, blockchain distributed ledger and smart contract technologies, behavioural and predictive analytics, the Internet of Things, platforms, Global Positioning Systems (GPS), biometric identifiers, augmented and virtual reality, robotics, and other relevant technologies.
  • Systems & Infrastructure: Data collection, capture, storage, processing and visualisation technologies; platforms and web services, mobile applications, meta-data, standards and interoperability, databases and data warehousing, high performance computing, algorithms, programming, decision support systems, user-interaction technologies, and other relevant topics.
  • Data Processing & Knowledge Generation: Data representation and pre-processing, integration, real-time and historical data analysis, mathematical and statistical models, ‘data-driven’ analysis, human-in-the-loop (HITL); mixed methodologies, secondary data analysis, web mining; Randomised Controlled Trials (RCTs), gaps in theory and practice, other relevant topics.
  • Policy for Data & Management: Data governance and regulatory frameworks; General Data Protection Regulation (GDPR); data collection, storage, curation and access; data security, ownership, linkage; data provenance and expiration; private/public sector/non-profit collaboration and partnership; capacity-building and knowledge sharing within government; institutional forms and regulatory tools for data governance.
  • Privacy, Security, Ethics & Law: Ethical concerns around data, algorithms, and interactions (both human-machine and machine-machine interactions) and associated technology responses; legal status of digital systems; bias, transparency and accountability of digital systems; public rights, free speech, dialogue and trust.

Full conference programme available here

Please also see presenters, session chairs and participants guidelines here

Programme overview:

Pre-Conference Workshops & Tutorials – Monday, June 10th
08:45 – 09:15 Arrivals & Registration
09:15 – 10:45 Morning Session Part 1
10:45 – 11:05 Break
11:05 – 12:35 Morning Session Part 2
12:35 – 13:30 Lunch
13:30 – 15:00 Afternoon Session Part 1
15:00 – 15:20 Break
15:20 – 16:50 Afternoon Session Part 2
Day 1 – Tuesday, June 11th  
08:45 – 09:15 Arrivals & Registration (Tea & Coffee)
09:15 – 09:30 Welcome & Introductions
09:30 – 09:50 Opening Keynote
09:50 – 11:00 Plenary Session 1
11:00 – 11:30 Break
11:30 – 12:30 Parallel Session 1
12:30 – 13:30 Lunch Break + Posters
13:30 – 14:30 Keynote Lecture 1
14:35 – 15:35 Parallel Session 2
15:35 – 16:00 Break
16:00 – 17:00 Parallel Session 3
17:30 – 19:30 Data & Policy Launch Reception
Day 2 – Wednesday, June 12th
09:00 – 09:30 Arrivals – Tea & Coffee
09:30 – 10:30 Parallel Session 4
10:30 – 11:00 Break
11:00 – 12:00 Keynote Lecture 2 & GovTech Lab Introduction
12:00 – 13:00 Lunch Break + Posters
13:00 – 14:00 Parallel Session 5
14:05 – 15:05 Parallel Session 6
15:05 – 15:35 Break
15:35 – 16:45 Plenary Session 2
16:45 – 17:00 Closing Remarks

Conference Chairs:

Zeynep Engin – Data for Policy, University College London
Jon Crowcroft – University of Cambridge, Alan Turing Institute
Stefaan Verhulst – The GovLab, New York University

Workshops/Tutorials Chair:

C. Leigh Anderson – University of Washington

Chair for UCL Posters & Demos Session:

Geraint Rees – University College London

International Organisation Committee:

Emanuele Baldacci – European Commission
Jon Crowcroft – University of Cambridge, Alan Turing Institute
Zeynep Engin – University College London
Innar Liiv – Tallinn University of Technology, Estonia
Stefaan Verhulst – New York University
Barbara Ubaldi – OECD, Paris

Special Track Chairs:

Anil Bharath – Imperial College London
Bilal Gokpinar – University College London
Catherine Mulligan – World Economic Forum, UN Digital Cooperation
Tom Smith– ONS Data Science Campus

Advisory Committee:

Niall Adams– Imperial College London
C. Leigh Anderson – University of Washington
Jean Bacon – University of Cambridge
Kenneth Benoit – London School of Economics and Political Science
Gabrielle Demange – Paris School of Economics
Anthony Finkelstein – UK Government Office for Science
Rayid Ghani – University of Chicago
David Hand – Winton Capital Management; Imperial College
Helen Margetts – University of Oxford; The Alan Turing Institute
Beth Noveck – New York University
Alan Penn – University College London
Rob Procter – University of Warwick; The Alan Turing Institute
Peter Smith – University of Southampton
Tom Smith – Office for National Statistics, UK
John Shawe-Taylor – University College London
John Taysom – Privitar
Philip Treleaven– University College London
Sir David Wallace – University of Cambridge
Dame Alison Wolf – King’s College London
Derek Wyatt – Royal Trinity Hospice; All Party Parliamentary Group on Data Analytics
Milan Vojnovic– London School of Economics and Political Science

Programme Committee:

Thomas Baar – University of Leiden
David Bounie – Telecom ParisTech
Daniel Castro – Centre for Data Innovation
Suleyman Demirsoy – Intel
Yves-Alexandre de Montjoye – Imperial College London
Seth Flaxman – Imperial College London
Jasmine Grimsley – Office for National Statistics, UK
Jose Manuel Magallanes – University of Washington; Pontificia Universidad Catolica del Peru
Scott Matthews – Carnegie Mellon University
Eric T. Meyer – The University of Texas at Austin, University of Oxford
Slava Mikhaylov – University of Essex
Suzy Moat – University of Warwick; The Alan Turing Institute
Mirco Musolesi – University College London; The Alan Turing Institute
Martijn Poel – Ministry of Education, Culture and Science, the Netherlands
Tobias Preis – University of Warwick; The Alan Turing Institute
Ralph Schroder – University of Oxford
Jatinder Singh – University of Cambridge
Akin Unver – Kadir Has University
Michael Veale – University College London
Diana Vlad-Calcic – European Commission
Andrew Young – New York University
Louisa Zanoun – UK Science and Innovation Network

UCL Local Committee:

Lauro Bovo – Innovation & Enterprise
Graca Carvalho – Strategic Alliances
Sarah Chaytor – Office of the UCL Vice-Provost (Research)
Louise Chisholm – E-research Domain
George Dibb – Industrial Strategy & Policy Engagement
Siobhan Morris – Global Challenges for Justice and Equality
Olivia Stevenson – UCL Public Policy

Conference Manager:

Emre Kazim – Data for Policy & GovTech Lab, UCL

Chair: Prof. Leigh C. Anderson 
Date/Venue: June 10, 2019 – University College London

 

  • “Understanding the Data & Curation choices behind the Indicator: SDGs & LSMS-ISA Measures of Progress  – Instructor(s): Ayala Wineman and Leigh Anderson; University of Washington
  • “Digital Ethics & Algorithm Assessment”– Instructor(s): Zeynep Engin and Adriano S. Koshiyama; University College London
  • “Data Stewardship in Action: Workshop on Making Data Collaboratives Systematic, Sustainable & Responsible” – Instructor(s): Stefaan Verhulst, The GovLab, New York University
  • “Introduction to Artificial Intelligence in Government” – Instructor(s): Jasmine Grimsley and Isabela Breton, Data Science Campus, Office for National Statistics, and, Barbara Webber – Cabinet Office
  • “Big-Data for Policy Making & Digital Transformation”– Instructor(s): Francesco Mureddu, Lisbon Council, Italy
  • “Collaborating with Universities: Marriage made in heaven?”– Instructor(s): Olga Sergushova , Vania Sena and Gina Yannitell Reinhardt; University of Essex
  • “Data Sharing & Data Trusts”– Instructor(s): Gefion Thuermer, Johanna Walker; University of Southampton, Peter Wells; Open Data Institute, and Kieron O’Hara; University of Southampton

 

Details of the Workshops and Tutorials:

  • Understanding the data & curation choices behind the Indicator: SDGs & LSMS-ISA measures of progress (Morning)

Ayala Wineman – University of Washington

Seldom visible are the steps necessary to prepare raw data for its many uses: analysis, merging with other data, training machines, or tracking progress in dashboards. These “data curation” decisions include cleaning, construction, and display choices.   In this workshop we illustrate how data curation choices can dramatically alter final numbers and interpretations, and hence affect investment and policy decisions. We discuss these issues in the context of several Sustainable Development Goals, and using examples from the Living Standards Measurement Study, Integrated Surveys on Agriculture in Ethiopia, Tanzania and Nigeria.

Aims and Objectives

For senior decision-makers our goal is for attendees to become more critical consumers of indicators.  We first illustrate how large differences in average estimates can arise from how data outliers are managed (e.g., winsorizing vs. median absolute deviation). We then work through how simple proxy choices and definitions of these measures themselves can vary in ways that matter to policy. Rural, for example, may be defined by political or administrative units (e.g. Prime Minister’s Office-Regional Administration and Local Government); by human settlements (e.g. adopted by the Ministry of Lands and Human Settlements Development), statistically (e.g. adopted by the National Bureau of Statistics), or simply by population density (Muzzini, 2008). We conclude by examining the potential consequences of two data trends: the increasingly common tendency to use these indicators in dashboards that compare, for example, outcomes across regions; and recent research efforts to use meta-analyses to synthesize findings across multiple studies.

Intended Audience

Policy-makers, practitioners, foundation, and other decision-makers who use SDG and other constructed indicators and compiled data to track progress, invest, or otherwise as evidence and inputs into decision-making. The workshop is also appropriate for producers of these indicators, to consider their cleaning and display choices. For those with training in Stata, .do files are available.

  • Digital Ethics & Algorithm Assessment (Morning)

Zeynep Engin and Adriano S. Koshiyama – University College London

This tutorial will be split into two parts:

  1. Overview of Digital Ethics & Key Concepts: the first part of the session will concentrate on providing an overview of the Digital Ethics landscape visiting a range of issues around data, algorithms and all types of interactions in this space. It will then introduce the key concepts of this growing field of engineering and computer science – including algorithmic bias and discrimination, privacy, explainability, regulation, and legality. The concepts will be introduced through exploring case studies from a diverse range of application domains including recruitment, advertising, banking, policing and criminal justice system.
  2. Technical Requisites for Algorithm Assessment: we aim to equip the attendees with a factsheet on what to request in numbers and written declarations during an algorithm risk assessment. In particular, it will focus on Fairness, Transparency and Robustness (FTR), arguably the three most important, well researched and technically developed topics. For each RTR construct we will present (i) its conceptual and/or mathematical definition; (ii) outline the main questions it is applied to; (iii) present how it is fitted in a case study; and (iv) what type of numbers, charts, explanations, etc. a digital ethics analyst would need to come up with a proper assessment.

Course aims and objectives

By the end of this tutorial we expect that the participant will understand the key concepts that underlie Digital Ethics, Fairness, Transparency and Robustness. Also, the participant will be capable of recognizing applications where these concepts can be used to refine automated decision-making (finance, government, retail, healthcare, etc.). As a final learning outcome, the attendee will be able to evaluate an algorithm from a Fairness, Transparency and Robustness perspectives, and come up with an initial judgment of its appropriateness to a specific problem.

Intended Audience

Policy-makers, practitioners and anyone with an interest in becoming more familiar with the language and principles of digital ethics.

  • Data Stewardship in Action: Workshop on Making Data Collaboratives Systematic, Sustainable & Responsible(Morning) 

Stefaan Verhulst – The GovLab, New York University     

There are many predictable uses for data collected and held by the private sector that can transform public policy. These traditionally untapped data assets have fuelled interest in “data collaboratives.”

Today, establishing and sustaining these new collaborative and accountable approaches requires significant time, effort, and resources for both data holders on the supply side and institutions representing the demand. By establishing data stewardship as a function, recognized within the private sector as a valued responsibility, Data Collaboratives can become more predictable, scaleable, sustainable and de-risked.

Session 

This session will take stock and review existing efforts of data stewardship to professionalize and systematize the cross-sector exchange of data to create new public value. Participants will brainstorm actionable approaches for responsibly sharing corporate data to address a specific, concrete public problem raised by a public official who can ground the conversation in a real issue.

Through this discussion, the participants will also reflect on lessons learned and previous practice to share broader lessons regarding data collaboration and data stewardship. Participants will consider the value proposition(s) of these emerging practices; questions related to risks and mitigation strategies; technical, legal, and cultural barriers and challenges; tools and methodologies for creating an impact through data collaboration and stewardship; best practices for achieving sustainability; and innovative metrics of success and evaluation techniques, among other issues.

Outcomes 

Participants will become familiar with public/private data collaboratives, as well as corporate data steward practices through a discussion focused on solving a real-world problem. They will also learn about the challenges and demands in establishing sustainable and meaningful partnerships.

The session will deliver several approaches to making data collaboratives more systematic, sustainable and responsible and provide an avenue for post-workshop engagement with all participants interested in prototyping solutions.

In addition to sharing knowledge on tools, methodologies, and questions related to data stewardship, the workshop will inspire Data for Policy participants to consider new data-driven approaches for solving public problems. It aims to increase experimentation and leveraging the insights and tools discussed in the workshop to decrease the transaction cost, time, and energy needed to establish data collaboratives.

  • Introduction to Artificial Intelligence in Government (Afternoon)

Jasmine Grimsley, Isabela Breton – Office for National Statistics, and Barbara Webber – Cabinet Office

This course will be delivered in collaboration between the Cabinet Office/GDS Academy and the Data Science Campus at the Office for National Statistics. It is targeted at anyone interested in understanding what AI is, how it’s being used in government now and how to get started with AI.

Course aims and objectives

This course will deepen your knowledge of the use of AI in government. By the end of the course, you will be able to:

  • describe AI in terms of machine learning, deep learning and robotic process automation, and identify what is needed for each to be successful
  • recognise key terms such as supervised and unsupervised learning
  • discuss the importance of ethics and transparency in using this new technology in government
  • explain the facts of AI and dispel myths and science fiction

Intended Audience

This 3 hour course is suitable for anybody interested in what AI can do. It would be of interest to leaders in a non-digital environment as well as practitioners who are considering robotic process automation or AI. This workshop will introduce AI and machine learning, discuss some myths in AI, ethics and where to get started in government.

  • Big-Data for Policy Making & Digital Transformation (Afternoon)

Francesco Mureddu – Lisbon Council, Italy

It is expected that a particularly important actor, such as the public sector, should constitute a successful disruption paradigm through adopting novel approaches and state-of-the-art ICTs to use data and help establish new types of evidence-informed policies. However, despite continuous investments and initiatives in the public sector, it is hard to allege that “we are already there” when it comes to full exploitation of data towards aiding the public sector to meet the emerging societal challenges. Therefore, the workshop would like to offer its perspective on how barriers that impede big data driven modernisation in policy making can be overcome.

Course aims and objectives

The aim of the workshop is to train relevant stakeholders in the use of Big Data in the Policy Making, explaining in particular the disruption that such technology can bring to public administration.

The workshop aims at explaining how to renovate the public sector on a cross-border level by presenting methods, technologies, tools and applications from both the public & the private sector, stepping on the power of open innovation and the rich opportunities for analysis and informed policy making generated by big data.

We will propose short and midterm milestones and relevant actions needed towards achieving the expected impacts for the public sector and society at large, by discussing key policy making challenges of how to: • Extract citizens’ opinions • Identify real-time proxies for official statistics • Anticipate detection of problems • Uncover causal relationships behind policy issues • Anticipate or monitor in real time the impact of policies • Identify key stakeholders to be involved in or target by specific policies • Generate a fruitful involvement of citizens in the policy making activity

Intended Audience

This course is suitable for policy-makers, public sector practitioners, and others particularly interested in Horizon 2020 and understanding value co-creation in public services for transforming European public Administrations.

  • Collaborating with Universities: Marriage made in Heaven?(Afternoon)

Olga Sergushova, Vania Sena and Gina Yannitell Reinhardt – University of Essex

The Catalyst Project, funded by the Higher Education Funding Council for England (HEFCE) and monitored by the Office for Students (OfS) is a partnership between the University of Essex and the County Councils for Essex and Suffolk that uses cross-disciplinary expertise in data analytics to assess risks for vulnerable members of the community and provide evaluation techniques to fully understand the impact of Council initiatives.

Course aims and objectives

To illustrate the benefits and risks of partnerships between Universities and Councils, by presenting two interactive predictive and evaluative examples:

  1. The Catalyst Project’s Risk Stratification team partnered with the Suffolk Multi Agency Safeguarding Hub (MASH) to build and test a machine learning approach (algorithm). This session will include short talks from representatives of both organisations, a hands on demonstration of a risk model platform, and discussion of benefits/risks of using live data. Participants will be invited to interact with the platform and play with a mock up dataset.  We use this example to discuss how to achieve data sharing agreements, deal with confidentiality issues, and communicate predictive analysis and machine learning techniques and outputs to lay users.

2) The recently developed Spotlight Evaluation Toolkit is an easy to access and streamlined evaluation process for policy makers and public commissioners. We begin with an introduction to evaluation, and an overview of different approaches and assessments of evaluation’s importance given risks and limited budgets. We then use the ‘Accidental Dwelling Fires in Essex’ as an example, with  commentary from the Essex Fire and Rescue Home Safety team describing how working with an academic partner has ensured that evaluation has been integrated into their Fire Service approach, and will become vital in  reporting prevention activity. Participants can test the tool and can start to develop an evaluation framework for one of their programmes as part of the session.

Intended Audience

Policy-makers, public programme commissioners, and others interested in potential university partnerships, and examples of methods and tools for using predictive analysis and evaluation to support public sector decision-making.

  • Data Sharing & Data Trusts (Afternoon; please note that this was previously advertised as a morning session)

Gefion Thuermer, Johanna Walker; University of Southampton, Peter Wells; Open Data Institute, and Kieron O’Hara; University of Southampton

Course aims and objectives

The goal of the workshop is to enable participants to

  1. a) Recognise the benefits of and obstacles to data sharing for themselves
  2. b) Define why they may or may not want to share data
  3. c) If they do want to share data, develop the grand picture of how this should be done

This will be achieved through a series of talks, case studies and activities. The talks will provide an overview of the subject area, and ensure all participants are on the same page. A case study will then exemplify the individual aspects of data sharing. The activities will guide attendees to collaboratively answer key questions about data sharing:

  1. What is data sharing, where and how can or should data be shared
  2. What role trust plays in data sharing and how it is generated and maintained
  3. Which models of data access and sharing are suitable in different circumstances

Participants from different backgrounds will learn from the combined expertise of the researchers from the University of Southampton, the practitioners from the ODI, and also from each other. This will enable them to understand data sharing not only from their own perspective, but also from the perspective of others with whom they might share data.

Intended Audience

This 3 hour course is suitable for anybody interested in data sharing. Participants should have some general awareness of data sharing. This could be as practitioners, where they share, receive, or otherwise work with data; as researchers, where they collect, analyse, or attempt to access data; or as policy makers, who work on the regulation of data sharing processes.

 

2019 Conference – Published Papers

Thornton, Michael. (2019). A Theory of Informational Wellbeing. Zenodo. http://doi.org/10.5281/zenodo.3245075 Read more>>

Thomas Roca. (2019, April 23). Identifying AI talents among LinkedIn members, A machine learning approach. Zenodo. http://doi.org/10.5281/zenodo.3240963 Read more>>

Liccardi Alexandre, Coudercy Laurent, Breton Laurent, Hissel François, & Lagarde Pierre. (2019). Managing sensitive data for public information: a use cases review of the French water information system. Zenodo. http://doi.org/10.5281/zenodo.3239060 Read more>>

Yulistina Riyadi, Dikara Alkarisya, & Deepakshi Rawat. (2019). The Potential of Crowdsourcing to Advance the SDGs by Fostering Local and Global Collaboration. Zenodo. http://doi.org/10.5281/zenodo.3238601 Read more>>

Harris, Swee Leng. (2019). Data Protection Impact Assessments as Rule of Law Governance Mechanisms. Zenodo. http://doi.org/10.5281/zenodo.3237865 Read more>>

Janssen, Heleen, Cobbe, Jennifer, Norval, Chris, & Singh, Jatinder. (2019). Personal Data Stores and the GDPR’s lawful grounds for processing personal data. Zenodo. http://doi.org/10.5281/zenodo.3234902 Read more>>

Stalla-Bourdillon, Sophie, Thuermer, Gefion, Walker, Johanna C., & Carmichael. Laura. (2019). Data Protection by Design: Building the foundations of trustworthy data sharing. Zenodo. http://doi.org/10.5281/zenodo.3079895 Read more>>

Dutt, Vaibhav, Sil, Srijan, Krishna, Harsha, & Palavalli, Bharath. (2019). Imagining Futures – A generative scenario-based methodology to improve planning and decision-support systems for policymakers. Zenodo. http://doi.org/10.5281/zenodo.3066348 Read more>>

Ricciato, Fabio, & Wirthmann, Albrecht. (2019). Trusted Smart Statistics: how new data will change official statistics. Zenodo. http://doi.org/10.5281/zenodo.3066061 Read more>>

McCarthy, Natasha, & Fourniol, Franck. (2019). The Role of Technology in Governance: the Example of Privacy Enhancing Technologies. Zenodo. http://doi.org/10.5281/zenodo.3056448 Read more>>

David Lopez, Alan W Brown, David Plans, & Phil Godsiff. (2019). Trust-building in digital health: An exploration of the business case for Block Chain technologies. Zenodo. http://doi.org/10.5281/zenodo.2864220 Read more>>

Jay, Matthew, Pearson, Rachel, Wijlaars, Linda, Olhede, Sofia, & Gilbert, Ruth. (2019, May 1). On the challenges of using administrative data from social care: experience using a new, linked whole-population dataset. Zenodo. http://doi.org/10.5281/zenodo.2810973 Read more>>

Nochta, Timea, Badstuber, Nicole Elizabeth, & Wan, Li. (2019). Evidence-informed decisionmaking in multi-stakeholder settings: The case of city digital twins for planning and management. Zenodo. http://doi.org/10.5281/zenodo.2798858 Read more>>

Thornton, Lauren, Neumann, Victoria, Blair, Gordon, Davies, Nigel, & Watkins, John. (2019). Trusted Brokers?: Identifying the Challenges Facing Data Centres. Zenodo. http://doi.org/10.5281/zenodo.2798468 Read more>>

Sielker, Franziska, & Sichel, Amarynth. (2019). Future cities in the making: overcoming barriers to information modelling in socially responsible cities. Zenodo. http://doi.org/10.5281/zenodo.2796506 Read more>>

Kruk Rink, Chantillon Maxim, Simonofski Anthony, Tombal Thomas, & Crompvoets Joep. (2019). FLEXPUB: Developing a Strategy for Flexible and Innovative e-Services. Zenodo. http://doi.org/10.5281/zenodo.2788922 Read more>>

Jacobs, Naomi, Edwards, Peter, Markovic, Milan, Caitlin D Cottrill, & Karen Salt. (2019). Public Sector Internet of Things Deployments: Value, Transparency, Risks and Challenges. Zenodo. http://doi.org/10.5281/zenodo.2713118 Read more>>

Peter Hill. (2019). Bracknell Town Council’s Carbon Reduction Working Group – using data to inform effective policy. Zenodo. http://doi.org/10.5281/zenodo.2775564 Read more>>

Bincoletto Giorgia. (2019). Data protection issues in cross-border interoperability of EHRs systems within the European Union. Zenodo. http://doi.org/10.5281/zenodo.2774486 Read more>>

Sapienza Salvatore. (2019). Transparency and Openness in Food Safety: insights about new Data Confidentiality rules. http://doi.org/10.5281/zenodo.2766359 Read more>>

Sander, Ina. (2019). Critical Big Data Literacy Tools – Engaging Citizens and Promoting Responsible Internet Usage. Zenodo. http://doi.org/10.5281/zenodo.2735058 Read more>>

Calzada, Igor, & Almirall, Esteve. (2019). Barcelona’s grassroots-led urban experimentation: Deciphering the ‘data commons’ policy scheme. Zenodo. http://doi.org/10.5281/zenodo.2604618 Read more>>

The Data for Policy 2019 Conference was hosted by University College London.

University College London
Wilkins Building
Gower St, Bloomsbury,
London WC1E 6BT

UCL is London’s leading multidisciplinary university. Founded in 1826 in the heart of London, UCL was the first university in England to welcome students of any religion and the first to welcome women on equal terms with men.

UCL operates in a global context and is committed to excellence, innovation and the promotion of global understanding in all of its activities: research, teaching, learning, enterprise and community engagement. Its distinctive approach seeks to inspire its community of staff, students and partners to transform how the world is understood, how knowledge is created and shared and the way that global problems are solved.

Transport:

UCL is located in the Bloomsbury district at the very centre of London.

Bus: UCL’s Gower Street site is served by many Transport for London bus routes. Buses travelling from north to south stop in Gower Street, immediately outside UCL’s main gate, while those travelling from south to north stop outside Warren Street station, about five minutes’ walk from UCL. Services to these stops include route numbers: 10, 14, 24, 29, 73, 134, 390.

London Underground: The closest tube stations to UCL’s Gower Street site are Euston Square (Hammersmith and City, Metropolitan and Circle lines), Warren Street (Northern and Victoria lines), Euston (Northern and Victoria lines) and Russell Square (Piccadilly line)

Rail Overground: London has many mainline rail stations. Most of these are a short journey away from UCL, with the stations at Euston, King’s Cross and St Pancras being within easy walking distance. Trains from London serve destinations across the UK.

Accomodation:

Delegates are responsible for securing their own accommodation whilst in London. There are numerous hotels and other accommodation options within walking distance of the Conference Centre.

Data for Policy 2019 Videos:

2019 Summary Videos: 

 

2019 Plenary Sessions and Keynotes:

     

2019 Workshop: Digital Ethics & Algorithm Assessment: 

 

2019 Extended Interviews: 

  

2019 Reflections:

               

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