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Data-intensive decision making in the era of big data and artificial intelligence

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Prof. Dr. Maria A. Wimmer
University of Koblenz-Landau, Germany
www.uni-Koblenz.de/agvinf
wimmer@uni-koblenz.de
Data-inten...

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Objectives of presentation
 Meta-view: Contextualising data-intensive decision-making in public
sector
 Where can AI and...

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Scope of decision-making in public spheres
2020/07/14 3Samos Summit 2020, (c) Prof. Dr. Maria A. Wimmer
Public Policy Maki...

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Data-intensive decision making in the era of big data and artificial intelligence

  1. 1. Prof. Dr. Maria A. Wimmer University of Koblenz-Landau, Germany www.uni-Koblenz.de/agvinf wimmer@uni-koblenz.de Data-intensive decision making in the era of big data and artificial intelligence Revolutionising digital governance???
  2. 2. Objectives of presentation  Meta-view: Contextualising data-intensive decision-making in public sector  Where can AI and Big Data be leveraged in decision-making  What future research and training needs can be identified (insights from Gov 3.0) 2020/07/14 2Samos Summit 2020, (c) Prof. Dr. Maria A. Wimmer
  3. 3. Scope of decision-making in public spheres 2020/07/14 3Samos Summit 2020, (c) Prof. Dr. Maria A. Wimmer Public Policy Making Law-Making Public Service Evaluation & Impact Assessment
  4. 4. Contextualizing decision-making in public spheres 2020/07/14 4Samos Summit 2020, (c) Prof. Dr. Maria A. Wimmer Decision-making along public policy making [Policy Lifecycle by Howlett, M., & Ramesh, M. Studying Public Policy: Policy Cycles and Policy Subsystems. Toronto: Oxford University Press, 1995]
  5. 5. Data as an asset AI and Big Data supporting Policy Decision-making 2020/07/14 5Samos Summit 2020, (c) Prof. Dr. Maria A. Wimmer [Policy Lifecycle by Howett and Ramesh, 1995 Data Asset Management Lifecycle: https://www.lean-data.nl/data-as-an-asset/] Analysis of Social Media for Opinion Mining, Sentiment Analysis and Agenda setting Text Mining for Policy making Mining Open Data for Policy making Leveraging structured and unstructured data … Data assets for data-driven policy analysis and modelling, supporting policy formulation and decision- making (e.g. using simulations of various kinds, gamification etc.), … AI-based support in policy decision-making Assessing and simulating different policy options and their consequences through AI and policy simulation …
  6. 6. Contextualizing decision-making in public spheres 2020/07/14 6Samos Summit 2020, (c) Prof. Dr. Maria A. Wimmer Decision-making along Law-making pcrocedures Second reading in Council Second reading in Parliament First reading in CouncilFirst reading in Parliament Commission proposal Conciliation [https://www.europarl.europa.eu/infographic/legislative-procedure/index_en.html] Third reading in the Parliament and Council
  7. 7. AI and Big Data supporting Law-making 2020/07/14 7Samos Summit 2020, (c) Prof. Dr. Maria A. Wimmer AI and Big Data Analytics for better informed Law-making AI and Big Data for ex-ante legal impact assessment Gamification in consultation and deliberation on draft bills Lots of data Lots of opinions Lots of findings from studies, monitoring, consultations … Lots of Expert Knowledge AI and ML Big Data Analytics Simulations & Gamification
  8. 8. Contextualizing decision-making in public spheres 2020/07/14 8Samos Summit 2020, (c) Prof. Dr. Maria A. Wimmer Decision-making in Implementation & Execution of Policy or Legislation Executive Bodies Public Administration Public Service Provisioning Citizens Companies, NGOs Observation & Monitoring §
  9. 9. Areas where AI and Big Data can support Policy Implementation and Implementation of Legislation 2020/07/14 9Samos Summit 2020, (c) Prof. Dr. Maria A. Wimmer AI and Robotics in automatic public service execution Robot process automation Expert systems supporting public servants in decisions along public service AI and Big data in civil protection and crime prevention (surveillance and cybercrime), … AI and Big data support in real-time interventions in society and market Big data analytics in monitoring the evolution of different policy domains: environment, health, climate, growth, finance and austerity, education, etc.
  10. 10. Improved Government and Governance Impact of Public Service Impact of Legislation Impact of Public Policy Intervention Needs from Monitoring/ Observation Ad-hoc Decision- making Needs (e.g. pandemic outbreaks) … Contextualizing decision-making in public spheres Decision-making in Evaluation and Revisions 2020/07/14 10Samos Summit 2020, (c) Prof. Dr. Maria A. Wimmer AI and ML Big Data Analytics Simulations & Gamification Dashboards
  11. 11. Areas where AI and Big Data can support Evaluation and Revisions of Policy / Legislation 2020/07/14 11Samos Summit 2020, (c) Prof. Dr. Maria A. Wimmer Automatic analysis and monitoring of performance of public service (in different policy domains) for better informed decision-making Analysis and simulation for ex-post policy impact assessment and ex-post legal impact assessment Big Data Analytics applied to [open and closed] Data repositories on people, companies, environment and other policy areas to identify needed policy interventions Dashboards for societal and market evolution, for monitoring the evolution in relevant policy areas, and to support better public policy-making and law- making Data … Data … Data … Data
  12. 12. Examples of AI use in Government: EC‘s AI Watch on Artificial Intelligence in Public Services 2020/07/14 12Samos Summit 2020, (c) Prof. Dr. Maria A. Wimmer [https://ec.eur opa.eu/jrc/en/ publication/ai- watch-2019- activity-report, p. 18]
  13. 13. Gov 3.0 – Roadmap for Research and Training Needs in Government 3.0 https://www.gov30.eu/
  14. 14. Roadmapping method Roadmap Research gaps & needs Training gaps & needs Scenarios Internet of things and Smart cities Artificial intelligence and machine learning Data driven policy modelling Virtual and augmented reality Natural language processing & sentiment analysis Etc. Blockchain Projectanalysisandsynthesisinvolving disruptivetechnologiesindigital government Insightsfromthedescriptionofdisruptive technologies Step 1 Step 2 Step 3 Step 4 [Wimmer, Viale Pereira, Ronzhyn, Spitzer (2020). Transforming Government by Leveraging Disruptive Technologies. In eJournal of eDemocracy and Open Government. 12 (1): 87-114]
  15. 15. Scenario examples  Included possible future implementations in AI, ML, NLP, IoT, AR/VR and Blockchain technologies  Implementations of smart city, gamification & co-creation of public services with the support of particular disruptive technologies
  16. 16. Workshop approach to identify research & training needs 04.09.2019 Discussing scenarios with disruptive technologies: Identifying research and training needs:
  17. 17. Research needs on disruptive technologies in Government 3.0 [Wimmer, Viale Pereira, Ronzhyn, Spitzer (2020). Transforming Government by Leveraging Disruptive Technologies. In eJournal of eDemocracy and Open Government. 12 (1): 87-114] AI/ML BigData IoT Gamification AR/VR NLP Blockchain Cloud(fog)Computing eID/eSignature SmartCity Co-creation CommunityAwarenes Platforms Once-onlyPrinciple Open(Linked) GovernmentData ServiceModules Gaming-basedPolicy Modellingand Simulation Standardisation and interoperability of disruptive technologies      Analysis of stakeholders                 Evaluation and policy making      Data security and data privacy       Automated decision-making   Ethical issues    Disruptive Technologies Concepts of Government 3.0 using disrupt. t.
  18. 18. Training needs on disruptive technologies in Government 3.0 [Wimmer, Viale Pereira, Ronzhyn, Spitzer (2020). Transforming Government by Leveraging Disruptive Technologies. In eJournal of eDemocracy and Open Government. 12 (1): 87-114] AI/ML BigData IoT Gamification AR/VR NLP Blockchain CloudComputing eID/eSignature SmartCity Co-creation CommunityAwarenes Platforms Once-onlyPrinciple Open(Linked) GovernmentData ServiceModules Gaming-basedPolicy Modellingand Simulation General technology skills       New technologies in public management & digital government    Management and economics capabilities on the use of disruptive technologies                 Capabilities in data science, data security and legal compliance            Capabilities in responsible research and in sustainability                 Disruptive Technologies Concepts of Government 3.0 using disrupt. t.
  19. 19. Challenges and outlook  AI and Big Data bear big potentials for revolutionising the way of public sector service towards more evidence-based data-driven decision-making  How these potentials can be leveraged effectively, and legally and socially compliant, still needs substantial interdisciplinary research efforts  For understanding the most influential phenomena of risks and success  For designing and implementing solutions that meet the needs and expectations of society  For understanding how the solutions work in practice and create impact 2020/07/14 19Samos Summit 2020, (c) Prof. Dr. Maria A. Wimmer
  20. 20. Challenges and outlook  AI Watch of JRC (EC) and a number of newly emerging case studies in research provide good insights  Multi-faceted challenges to overcome (see research and training needs identified in Gov 3.0)  Motivation to change, fear of being exposed to manipulation (algorithms) and misuse (data), and acceptance by people to be tackled (social issue)  Particular trade-off between exploiting machine intelligence and at the same time meeting the social needs of people 2020/07/14 20Samos Summit 2020, (c) Prof. Dr. Maria A. Wimmer Good balance between technological advancements and the pace of socially compatible digital transformation demands for interdisciplinary research
  21. 21. Many thanks for your attention! wimmer@uni-koblenz.de http://www.uni-koblenz.de/agvinf
  22. 22. With the use of AI and Big Data in policy-making, law-making and public service provisioning, will the ways of decision- making substantially change in the next 1-2 decades ? https://pingo.coactum.de/517165 PIN: 517165 Question for discussion: 2020/07/14 22Samos Summit 2020, (c) Prof. Dr. Maria A. Wimmer
  23. 23. Results from online query: Question 1 2020/07/14 23Samos Summit 2020, (c) Prof. Dr. Maria A. Wimmer
  24. 24. Results from online query: Question 2 2020/07/14 24Samos Summit 2020, (c) Prof. Dr. Maria A. Wimmer If your previous answer tended towards change, please describe what will change in decision-making by using AI and Big Data (Nr. of entries=17, usable answers by individuals= 16, nr. of answers analysed=26, multiple choice): Time to decisions Soft factors Input to decision-making Way of decision- making Results of decision-making Governance model Faster decision- making (3 occurences) Better meeting needs and problems; Increased equality; More transparency Easier decisions; Handling data will improve; Impact assessment; More evidence based on expertise; More informed drafting of regulation; More real-world data; More sentiment analysis; New information sources used in decision-making and policy-making; New source of legitimization of politicians' choices; Suggestions of most probable courses of action Automated decision-making will be more prevalent Less decisions based on intuition or ideology; Outcomes of decisions will change; Quicker evaluation and monitoring; Results more reliable Interaction with surroundings; Interconnectivity; Less hierarchy; More flexibility; Public servants' workforce will be restructured
  25. 25. Results from online query: Question 3 If your previous answer tended towards NO change, please explain why you think that decision-making procedures will not change by using AI and Big Data (8 participants answering):  No change in political decisions in the past 20 years / Tradition  Resistance of politicians to leave decisions to AI and big data / Resistance among public servants  Policy makers will not take decision-making with AI and big data seriously  Need to distinguish linear thinking and exponential thinking: if people will be able to learn to think more exponentially, we will have more parts changing because of AI  Data not being harmonized  Organisational and/or cultural hesitation  Interoperability of systems  Ethical concerns 2020/07/14 25Samos Summit 2020, (c) Prof. Dr. Maria A. Wimmer

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