Data for Policy 2017 Conference
Venue: Westminster Conference Centre (1VS)
Data for Policy is an independent initiative launched in 2015 at its inaugural conference “Policy-making in the Big Data Era: Opportunities and Challenges” that was hosted by the University of Cambridge. The second conference “Frontiers of Data Science for Government: Ideas, Practices, and Projections” was also held at the same venue with further support from the European Commission in 2016.
The series has proven to be a key international discussion forum around the theory and applications of Data Science as relevant to governments and policy research, and supported by a large number of key stakeholders including prestigious academic institutions, government departments, international agencies, non-profit institutions, and businesses.
The third of the Data for Policy conference series highlights ‘Government by Algorithm?’ as its main theme, presenting findings and discussions from the Data Science community in the form of research and policy presentations, workshops, fringe events and other innovative formats.
Topics of discussion will include:
- Government & Policy: Digital era governance and democracy, data-driven service delivery in central and local government, algorithmic governance/regulation, open source and open data movements, sharing economy, digital public, multinational companies (Google, Amazon, Uber) and privatization of public services, public opinion and participation in democratic processes, data literacy, policy laboratories, case studies and best practices.
- Policy for Data & Management: Data governance; data collection, storage, curation, and access; distributed databases and data streams, psychology and behaviour of decision; 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.
- Data Sources: Open, commercial, personal, proprietary sources; administrative data, official statistics, user-generated web content (blogs, wikis, discussion forums, posts, chats, tweets, podcasting, pins, digital images, video, audio files, advertisements, etc.), search engine data, data gathered by connected people and devices (e.g. wearable technology, mobile devices, Internet of Things), tracking data (including GPS/geolocation data, traffic and other transport sensor data, CCTV images etc.,), satellite and aerial imagery, and other relevant data sources.
- Data Analysis: Computational procedures for data collection, storage, and access; large-scale data processing, real-time and historical data analysis, spatial and temporal analysis, forecasting and nowcasting, dealing with biased/imperfect/missing/uncertain data, human interaction with data, statistical and computational models, networks & clustering, dealing with concept drift and dataset shift, other technical challenges, communicating results, visualisation, and other relevant analysis topics.
- Methodologies: Qualitative/quantitative/mixed methods, secondary data analysis, web mining, predictive models, randomised controlled trials, sentiment analysis, Blockchain distributed ledger and smart contract technologies, machine learning, Bayesian approaches and graphical models, biologically inspired models, simulation and modeling, small area estimation, correlation & causality based models, gaps in theory and practice, other relevant methods.
- Policy/Application Domains: Public administration, cities and urban analytics, policing and security, health, economy, finance, taxation, law, science and innovation, energy, environment, social policy areas (education, migration, etc.), humanitarian and development policy, crisis response, public engagement and other relevant domains.
- Citizen Empowerment: Online platforms and communities, crowdsourcing, citizen science, community driven research, citizen expertise for local & central decision-making, mobile applications, user communities, other relevant topics.
- Ethics, privacy, security: Data and algorithms in the law; licensing and ownership; using personal or proprietary data; transparency, accountability, participation in data processing; discrimination- and fairness-aware data mining and machine learning; privacy-enhancing technologies (PETs) in the public sector; public rights, free speech, dialogue and trust.
For more information and to purchase tickets, please visit the Data for Policy website.