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.