In 2021, Data for Policy established six non-domain specific and overarching areas of interest for the conference and journal. The areas are interrelated and do not indicate siloed activity. They are rather an articulation of the breadth and depth of the vision and mission for improved data-driven decisions and policymaking, which is the ethos of the Data for Policy community. The six areas are the basis for the conference’s standard tracks, as follows:
Standard Track (Area) 1: Data-driven Transformations in Policy and Governance – this area focuses on the high-level vision for philosophy, ideation, formulation and implementation of new approaches leading to paradigm shifts, innovation and efficiency gains in collective decision making processes. Topics may include:
- Data-driven innovation in public, private and voluntary sector governance and policy-making at all levels (international; national and local): applications for real-time management, future planning, and rethinking/reframing governance and policy-making in the digital era;
- Data and evidence-based policy-making;
- Government-private sector-citizen interactions: data and digital power dynamics, asymmetry of information; democracy, public opinion and deliberation; citizen services;
- Interactions between human, institutional and algorithmic decision-making processes, psychology and behaviour of decision-making;
- Global policy-making: global existential debates on utilizing data-driven innovation with impact beyond individual institutions and states;
- Socio-technical and cyber-physical systems, and their policy and governance implications.
The next three areas focus more specifically on the current methodologies, strategies and concerns that shape data-driven transformations in governance and policy.
Standard Track (Area) 2: Data Technologies and Analytics for Policy and Governance – this area is concerned with data in its variety of forms and sources, and infrastructure and methods for its utilisation in policy and governance:
- Data sources: Personal and proprietary data, administrative data and official statistics, open and public data, organic vs designed data, sensory and mobile data, digital footprints, crowdsourced data, and other relevant data;
- Technologies: Artificial Intelligence, Blockchain, Internet of Things, Platform Technologies, Digital Twins, Visualisation and User Interaction Technologies, data and analytics infrastructures, cloud and mobile technologies;
- Methodologies and Analytics: Mathematical and Statistical models, Computational Statistics, Machine Learning, Edge Analytics, Federated Learning, theory and data-driven knowledge generation, multiple disciplinary methodologies, real-time and historical data processing, geospatial analysis, simulation, gaps in theory and practice.
Standard Track (Area) 3: Policy Frameworks, Governance and Management of Data-driven Innovations – this area focusses on governance practices and management issues involved in implementation of data-driven solutions:
- Data and algorithm design principles and accountability
- Local, national and international governance models and frameworks for data and associated technologies;
- Data and algorithms in the law;
- General Data Protection Regulation (GDPR) and other regulatory frameworks;
- Data intermediaries, trusts and collaboratives;
- Meta-data, interoperability and standards;
- Data ownership, provenance, sharing, supply chains, linkage, curation and expiration;
- Data sovereignty and data spaces.
Standard Track (Area) 4: Ethics, Equity and Trust in Policy Data Interactions – this area examines the issues which must be considered in technology design and assessment:
- Digital Ethics: Data, algorithms, models and dynamic interactions between them;
- Digital trust, and human-data-machine interactions in policy context;
- Responsible technology design and assessment;
- Privacy and data sharing;
- Digital identification, personhood, and services;
- Uncertainties, bias, and imperfections in data and data-driven systems;
- Algorithmic behaviour: equity and fairness, transparency and explainability, accountability, and interpretability;
- Human-machine collaboration in strategic decision making and algorithm agency;
- Human control, rights, democratic values, and self-determination.
Beyond the above sector-agnostic classification of the research landscape, we establish the following two areas of primary interest in this space.
Standard Track (Area) 5: Algorithmic Governance
- Data-driven insights in governance decision making, black-box processing;
- Algorithm agency along with human and institutional decision-making processes;
- Government automation: citizen service delivery, supporting civil servants, managing national public records and physical infrastructure, statutes and compliance, and public policy development;
- Algorithmic ‘good’ governance: participation, consensus orientation, accountability, transparency, responsiveness, effectiveness and efficiency, equity and inclusiveness, and the rule of law.
Standard Track (Area) 6: Data to Tackle Global Issues and Dynamic Societal Threats
- Human existence and the planet;
- International collaboration for global risk management and disaster recovery;
- Global health, emergency response, Covid-19 and pandemics;
- Sustainable development, climate change and the environment;
- Humanitarian data science, international migration, gender-based issues and racial justice;
- International competition and cultures of digital transformation.