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BDVe Webinar Series - Big Data for Public Policy, the state of play - Data-driven policy making: methodology and experiment


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Do you know how data-driven approaches can influence the policy cycle and the benefits derived from this? Have you ever participated in a policy-lab, collaborating with other stakeholders to develop and test a policy? In this session, Anne Fleur van Veenstra from TNO will delve into current practices, insights and lessons learnt from current policy-lab projects, followed by Francesco Mureddu, from the Lisbon Council, who will look ahead and identify the main challenges and opportunities by presenting and discussing a roadmap for Future Research Directions in data-driven Policy Making.

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BDVe Webinar Series - Big Data for Public Policy, the state of play - Data-driven policy making: methodology and experiment

  2. 2. THERE IS A NEED TO RENEW THE LEGITIMACY OF PUBLIC POLICY-MAKING, ESPECIALLY THROUGH GREATER CITIZEN’S INVOLVEMENT AND OF DELIVERING BETTER PUBLIC SERVICES FOR ALL (ECOM 4614, 2016). 1. Data driven policy making 2. Policy Lab approach 3. Experiment: Rotterdam youth care Data-driven policy making: methodology and experiment
  3. 3. DATA DRIVEN POLICY MAKING Data such as (real-time) sensor data may provide new insights for (‘evidence-based’) policy making This may also require new methodologies, e.g. will machine learning enhance policy models? Co-creation: stakeholder involvement in different phases of the policy cycle Challenges include organizational readiness and policy makers’ willingness for using data and data-driven methods for policy making Data-driven policy making: methodology and experiment
  4. 4. THE ROLE OF EXPERIMENTS Legal restrictions, such as the GDPR, require a safe environment and methodology for experimenting: to explore the impact of new technologies and methodologies on public policy; to develop or enhance an evidence-base for policy; to involve policy makers and their organizations to investigate the opportunities and challenges that arise for scaling; and to involve citizens (and other stakeholders) in policy making (‘co-creation’) Data-driven policy making: methodology and experiment
  5. 5. POLICY LAB APPROACH 1. Identify new data sources and technologies that impact public policy. 2. Design experiments to test new technologies, methodologies and policy models. 3. Implement and monitor policy; develop opportunities for scaling. Data-driven policy making: methodology and experiment
  6. 6. CASE: YOUTH POLICY IN ROTTERDAM Data on social-emotional skills of youngsters may enhance the current policy model Develop a (double) hybrid policy model using theory and data and machine learning and ‘traditional’ statistics Data-driven policy making: methodology and experiment
  7. 7. Exploring new data sources and technologies and their impact on policy 1. Analyse the current theory-based policy model 2. Identify data sources that may enhance the policy model 3. Develop DPIA / data processing agreement 4. Gather and clean the data 5. Train the model (machine learning) 6. Perform statistical analyses 7. Analyse outcomes; explainability 8. Develop hybrid policy model PREDICT Data-driven policy making: methodology and experiment
  8. 8. MODEL FOR YOUNGSTERS’ SOCIAL EMOTIONAL CAPABILITY AND BEHAVIOUR 1. Based on literature, the municipality developed a conceptual model covering many factors that are of influence on the social-emotional capabilities of youngsters 2. For every aspect related to this factor, data sources were identified and a data collaboration was set up between multiple organizations supplying these data sets 3. Data analyses were carried out, both using machine learning and statistical methods 4. A hybrid policy model that is used to inform interventions was developed Data-driven policy making: methodology and experiment
  9. 9. OUTCOMES Not one variable could be found that would have the largest influence on factors related to social- emotional capabilities House value as a proxy for income was found to predict social emotional capabilities best Youngsters with higher attributed social emotional capabilities generally showed better behavior It was hard to obtain the necessary data Pre-process data was difficult and required iteration Multi-disciplinarity takes a lot of time! Data-driven policy making: methodology and experiment
  10. 10. REQUIREMENTS FOR SCALING Joint development of a data processing agreement is important for establishing trust ‘Top management support’ is essential Many aspects of the GDRP remain unclear and need to refined Differentiation between experiment and application in practice is important Legal grounds for experimentation differ between domains Application of findings to new data may include biases Multidisciplinarity is a requirement for useful outcomes Understanding of the domain, the model and the data is necessary An ‘agile’ way of working may support this Explainability of machine learning is a challenge Training the model is dependent on the data available Importance of a hybrid approach in which statistics test the machine learning precitions Data-driven policy making: methodology and experiment
  11. 11. IMPLICATIONS FOR POLICY Data is often gathered within a different context dan in which is it reused Data landscape often grew organically Technical interoperability; semantics en contextual understanding are all necessary for reuse of data New dependencies emerge: data is not always gathered within the same organization Cooperation in networks is required Organizations that were formerly a part of government now become data providers It is expected that the policy cycle will accelerate Different policy phases follow up more quickly and new links between phases emerge This also offers opportunities for rapid responses in case of undesired outcomes Collaboration in networks and co-creation require a large degree of openness In which way governments should become transparent: data, algorithm, via validation? Outcomes of analyses under increased scrutiny Data-driven policy making: methodology and experiment