Data for Policy 2019

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.

We also invite submissions for the following Special Tracks:
Trading Data for Health: Balancing Ethics, Economics and Technology – Track Chair: Anil Bharath, Imperial College London
Data Practices, Lessons and Challenges: A Private-Sector (Business) Perspective – Track Chair: Bilal Gokpinar, University College London
Blockchain & Data Governance – Track Chair: Catherine Mulligan, World Economic Forum, UN Digital Cooperation, Imperial, UCL
Successful Uses of Data and AI for Public Good – Track Chair: Tom Smith, Office for National Statistics, UK

Contributions can be proposed in the following categories (please see the guidelines for more details).

• Individual Research/Policy/Practitioner Proposals (1000-word max.): An extended abstract should be submitted, which includes a title, the research/policy question, the research methodology and data used, and key findings.
• Session Proposals (4500-word max.): Session proposals are welcome. This combines 3-4 presentations from researchers and/or practitioners each providing a max. 1000-word abstract. A max. 500-word description of the panel should also be submitted.
• Demo Proposals: The Demonstration Track is intended to provide an opportunity to showcase new tools, technological advances, and services offered in this emerging field. The contributions must demonstrate state-of-the-art technology and must be run live, preferably with some interactive parts. A max. 1000-word description of the session should be submitted, which includes the technology demonstrated, the elements of novelty, the live-action part, the interactive part, the equipment brought by the demonstrators, and the equipment required from the track organisers.
• Poster Submissions: All individual submissions to the conference will first be considered for oral presentation and then for poster sessions at the conference. Those who wish to make submission for the poster sessions only should make a standard submission indicating at the top that they are only interested in presenting a poster.Official conference website – dataforpolicy.org (http://dataforpolicy.org)

  Please note that this is a fee-paying event and all conference participants, including presenters, will be responsible for arranging their own travel and accommodation. We have limited funding to support student participation: those who wish to be considered for these grants should send a CV and cover letter explaining their case to team@dataforpolicy.org . This should be done after completion of abstract submission.