Discover how data, analytics, and AI will transform public services for the better

In AI and the Future of the Public Sector: The Creation of Public Sector 4.0, renowned executive and consultant Tony Boobier delivers a comprehensive reference of the most relevant and central issues regarding the adoption and implementation of AI in the public sector. In the book, you'll find out why data and analytics are the solution to significant and ongoing problems in the public service relating to its ability to effectively provide services in an environment of reduced funding. You'll also discover the likely impact of future technological developments, like 5G and quantum computing, as well as explore the future of healthcare and the effective digitalization of the healthcare industry.

The book also offers:

Discussions of policing 4.0 and how data and analytics will transform public safety
Explorations of the future of education and how ai can dramatically enhance educational standards while reducing costs
Treatments of the internationalization of public services and its impact on agencies and departments everywhere

A can't-miss resource for public sector employees at the managerial and professional levels, AI and the Future of the Public Sector is an insightful and timely blueprint to the effective use of artificial intelligence that belongs in the bookshelves of policy makers, academics, and public servants around the world.

TONY BOOBIER is an independent consultant and former IBM worldwide executive with over 30 years’ experience in the fields of financial services, business intelligence, and analytics. He holds professional qualifications in engineering, marketing, supply chain management, and insurance.

Contents

Acknowledgments

About the Author

Introduction

1. Understanding the Key Building Blocks of Progress

1.1. Introduction

1.2. Key Building Blocks of Data Science and AI

1.2.1. Data Acquisition

1.2.2. Data Maintenance

1.2.3. Analysis

1.2.4. Communication

1.2.5. Machine Learning

1.2.6. Artificial Intelligence

1.2.7. Risks and Advantages

1.2.8. Four Key Focuses for Future AI

1.3. Quantum Computing

1.3.1. What Is Quantum Computing?

1.3.2. Impact on Cybersecurity

1.4. Proliferation of Devices

1.5. 5G and the Impact of Advanced Communication

1.5.1. Global Transformation

1.6. Public Services 4.0

1.7. Conclusion

1.8. Notes

2. Office of Finance

2.1. Introduction

2.2. Forecasting and Public Finance Management

2.3. Forecasting

2.3.1. Qualitative Forecasting

2.3.2. Quantitative Forecasting

2.3.3. Forecasting for Public Sector Transformation

2.3.4. Managing Risk and Uncertainty

2.3.5. Forecasting in IT Projects

2.3.6. The Move Toward Activity-Based Costing

2.3.7. Hard Benefits and Soft Benefits

2.3.8. Enterprise Resource Planning

2.3.9. AI and Governmental Administration

2.3.10. Global Partnership on AI

2.4. Conclusion

2.5. Notes

3. Public Order and Safety

3.1. Introduction

3.2. The Future of Policing in an AI Era

3.2.1. Transformation of Police Work

3.2.2. Criminal Use of AI

3.2.3. Police Use of New Technologies

3.2.4. Case Studies in Policing

3.2.5. Policing in China

3.2.6. Forward-Looking Policing

3.3. AI in Policing

3.3.1. Impact on Police Behavior

3.4. The Citizen as a Key Component of Future Policing

3.5. Police and Location Analytics

3.6. Policing Summary

3.7. Border Security and AI

3.8. Customs Reform

3.8.1. The Citizen and Taxation

3.9. Fire Safety and AI

3.9.1. Natural Fire Prevention

3.9.2. Prevention of Urban Fires

3.9.3. Smart Homes and Fire Protection

3.9.4. Commercial Fire Protection

3.9.5. Firefighting Using AI

3.9.6. Fire Station Locations

3.10. Conclusion

3.11. Notes

4. Personal Social Services

4.1. Introduction

4.2. Care Homes

4.2.1. The UK Model

4.2.2. Care Homes in Japan

4.2.3. The Canadian Picture

4.2.4. The Emergence of AgeTech

4.2.5. Going Forward

4.2.6. Conclusion

4.3. Impact on Children

4.4. Mental Health

4.5. Social Protection

4.5.1. Social Risk Framework

4.6. Employment and Benefit Management

4.7. Conclusion

4.8. Notes

5. Health

5.1. Introduction

5.2. Digitalization and Its Importance in Healthcare

5.2.1. Different Categories of Data Sources in Healthcare

5.3. Medical Monitoring and Biosensors

5.3.1. Use of Biosensors in Mental Health

5.4. Innovating to Zero in Healthcare

5.4.1. Zero Invasive Surgery

5.4.2. Zero Waste Management

5.4.3. Zero Surgical Errors

5.5. Tissue Engineering

5.6. Cybernetics

5.7. Advancement in Drug Creation and Treatment

5.8. Case Studies in Healthcare

5.8.1. Ping An Good Doctor

5.8.2. Cancer Screening

5.9. Paramedics and AI

5.10. Cybersecurity in Healthcare

5.11. Conclusion

5.12. Notes

6. Education

6.1. Introduction

6.2. Learning for the Future

6.3. Teaching in the Future

6.3.1. The Use of AI for Predicting Exam Success

6.4. AI and Language in the Classroom

6.4.1. Automatic Essay Scoring

6.4.2. Removing Communication Barriers

6.5. Robots in the Classroom

6.6. The Shortage of Tech Talent

6.7. Case Studies in Education

6.8. Conclusion

6.9. Notes

7. Defense

7.1. Introduction

7.2. Use Cases of AI in Defense

7.2.1. Intelligence, Surveillance, and Reconnaissance

7.2.2. Logistics

7.2.3. Cyberspace Operations

7.2.4. Information Operations and “Deep Fakes”

7.2.5. Command and Control

7.2.6. AI and Augmented Reality Soldiers

7.2.7. Semi-Autonomous and Autonomous Vehicles

7.3. Ethical Issues

7.4. Drones

7.5. Conclusion

7.6. Notes

8. Smarter Cities and Transportation

8.1. Introduction

8.2. Smarter Cities

8.2.1. Smart Infrastructure

8.2.2. Smart Transportation

8.2.3. Street Lighting

8.2.4. Water Utilities

8.2.5. Emergency Services

8.2.6. Waste Collection and Disposal

8.2.7. Maintenance of Public Places

8.2.8. Humans as Devices

8.2.9. Data Challenges for Smarter Cities

8.3. Transportation

8.3.1. Traffic Management

8.3.2. Road Safety

8.3.3. Highway Maintenance

8.3.4. Autonomous Trams

8.3.5. Autonomous Taxis

8.4. Railways and the Future of Rail

8.4.1. Net Zero in Rail

8.4.2. AI and Effective Rail Timetabling

8.5. Conclusion

8.6. Conclusion

8.7. Notes

9. Housing and the Environment

9.1. Introduction

9.2. AI in Social Housing

9.2.1. Risk Management in Social Housing

9.2.2. Transforming the Tenant Experience

9.2.3. Case Study – Housemark Pilot

9.2.4. Social Housing Fraud

9.2.5. Tenant Viewpoint

9.2.6. AI as a Virtual Housing Assistant

9.2.7. Chatbots in Social Housing

9.3. AI and the Environment

9.4. Management of Natural Disasters

9.4.1. Flooding and Flood Management

9.4.2. Flood Defense

9.4.3. Earthquakes, Windstorms, and Forest Fires

9.5. Conclusion

9.6. Notes

10. Employment, Industry, and Agriculture

10.1. Introduction

10.2. Employment

10.2.1. Unemployment

10.3. AI and Industry

10.3.1. State-Owned Enterprises

10.3.2. China Model

10.3.3. South African Model

10.3.4. UK Model

10.3.5. SOEs in the United States

10.4. Agriculture

10.4.1. The Role of AI in Agricultural Policy

10.4.2. AI in Agricultural Issues

10.5. Conclusion

10.6. Notes

11. The Role of the State

11.1. Introduction

11.2. What Is the Role of the State?

11.3. What Is Surveillance?

11.4. Reasons for Surveillance

11.5. Surveillance Capitalism

11.6. Surveillance in Covid “Track and Trace”

11.7. Data Justice and Independent Oversight

11.8. A Contrary View

11.9. The Ethics of Surveillance

11.10. Nudging the Citizen

11.11. Conclusion

11.12. Notes

12. Risk and Cybercrime

12.1. Introduction

12.2. The Nature of Risk

12.2.1. Management of Risk

12.2.2. Three Levels of Risk Defense

12.3. Roles and Responsibilities in the Public Sector

12.4. Examples of Risk

12.4.1. Technology and System Failure

12.4.2. Data Security and Privacy

12.4.3. Employee Error

12.4.4. Failure of Processes, Systems, and Policies

12.4.5. Reputational Risk

12.4.6. External Risk

12.5. Cybercrime in the Public Sector

12.6. Prevention of Cybercrime and Protection from It

12.6.1. Air Gapping

12.6.2. Supply Chain Vulnerability

12.6.3. Impact on Insurance Coverage

12.7. Use of AI in Managing Risk

12.8. Conclusion

12.9. Notes

13. Implementation – Leadership and Management

13.1. Introduction

13.2. Leadership

13.2.1. Transfer of Private Sector Leaders to the Public Sector

13.3. Leaders or Managers?

13.4. Managing the Mission

13.4.1. Creating the Mission

13.4.2. Prioritization: Where to Start?

13.4.3. Communicating the Mission Statement

13.5. Management of Resources

13.5.1. Technical versus Traditional

13.5.2. Specialist versus Generalist

13.5.3. Training and Education

13.6. Management of Key Stakeholders

13.6.1. Worker Representation and Trade Unions

13.6.2. US Policy Recommendations

13.6.3. German Policy Recommendations

13.6.4. “Dignity at Work” and Working from Home

13.7. Conclusion

13.8. Notes

14. Further Implementation Issues

14.1. Introduction

14.2. A Theoretical Approach to Change

14.3. Managing the Problem of Bias

14.3.1. Data Exclusion from Marginalized Communities

14.3.2. Locational Data Issues

14.4. Operational Considerations

14.4.1. Piloting and Test Running the System

14.4.2. Measuring Benefit

14.4.3. Independent Review

14.5. Outsourcing, Partnering, and Supply Chain Management

14.6. The Concept of “Nudge”

14.7. Global Considerations

14.8. Conclusion

14.9. Notes

15. Conclusions

15.1. Reflections

15.2. AI and the Real Pace of Change

15.3. Measuring ROI – More Art Than Science?

15.4. AI and Stimulation of Wider Reforms

15.5. The Role of Government in Public Sector Transformation

15.6. Moving the Goalposts

15.7. Notes

Appendix A The Seven Principles of Public Life

Appendix B Transformation Roadmap for Public Services

Appendix C List of Tables

Appendix D List of Figures

Index

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