Platforms and Artificial Intelligence: The Next Generation of Competences

Foreword
Acknowledgements
Introduction
Contents
Part I: Platforms, Platformisation and the Foundations of Their Business Models
	Digital Platform Modelling: Delineating the Foundations of Their Business Models
		1 The Context and Challenges
			1.1 Benefits
		2 Analytical Approaches
			2.1 Defining a Platform: Key Characteristics
			2.2 Disciplinary Approaches
				2.2.1 The Industrial Economics Approach
					The Main Arguments in the Economics Literature
					A Dynamic/Strategic Approach to Platforms
					Differences Between Platforms and Traditional Vertical Businesses
					Trust, Regulation and Governance
					Sharing Economy Platforms
					Incentive to Contribute
					Innovation and Crowdsourcing
					Incentives to Innovate
					Market Entry, Competition Policy
					The Winner-Takes-All Argument
					Platforms as a Substitute for Public Services?
				2.2.2 The Engineering Approach
			2.3 Platforms as a Set of Strategic Choices
				2.3.1 Openness Versus Control
			2.4 Platforms as a Lever for Ecosystem Resources
				2.4.1 Why Small Firms Join Leading Platforms
				2.4.2 How to Invest and Lead a Platform, Notably by Investing in Knowledge
				2.4.3 Platform Strategy and Design as a Strategic Choice
			2.5 Attention and Data: Key Assets
		3 Typologies of Platforms
		4 Platforms in the Literature
		5 Towards an Integrative Approach
			5.1 How Do Platforms Contribute to Innovation?
				5.1.1 Analysing Products/Services and Services´ Variety
					The Institutional Perspective
					Platformization and Traditional Industries
					The Platformization of the Public Sector: The State as a Platform
			5.2 Digital Platforms and Policy
				5.2.1 Market Competition
				5.2.2 Labour
				5.2.3 Data Security
			5.3 Questions for Further Research
				5.3.1 Innovation Practices
				5.3.2 Assessment of Resources and the Impact on Market Structures
					The Demand Side
					The Internal/Organizational Side
					The Supply Side
		References
	Growth of Internet Digital Platforms in China: Stages, Trends, and Research Opportunities
		1 Introduction
		2 Functional Types and Evolution Stages
			2.1 Basic Functional Types of Digital Platforms
			2.2 Evolution Stages
		3 An Institutional Perspective
		4 Trends and Research Opportunities
		References
	Platforms, AI and the Spillover Effect
		1 Introduction
		2 Knowledge Diffusion Between Large Firms, SMEs and Platforms
		3 Patents and Technological Diffusion
		4 Data
		5 The Model
			5.1 Spillover Definition
			5.2 Econometric Specification
		6 Results
		7 Conclusion
		Appendix
		References
Part II: Artificial Intelligence, Technological Tendencies and the Policy Agenda
	Artificial Intelligence: A Review of the Economic Context and Policy Agenda
		1 Introduction
		2 Digital Innovation, AI and the Economy
			2.1 Data as a Core Input for Innovation
				2.1.1 Enabling New Services and Business Models
				2.1.2 Enhancing Customisation
				2.1.3 Optimising Processes
			2.2 Faster Innovation Cycles
				2.2.1 Designing, Prototyping and Testing New Products and Services
				2.2.2 Experimenting with (Not Fully Finished) Products and Services on the Market
				2.2.3 Regular Upgrading and Versioning
				2.2.4 Personalisation
			2.3 Collaborative Innovation
				2.3.1 Data Sharing
			2.4 Specific Features of AI Within the Field of Digital Technologies
		3 Government Policies for Digitalisation and AI
			3.1 Digital Innovation Policy Programmes
				3.1.1 Target Groups
				3.1.2 Priority Technologies and Industries
				3.1.3 Main Instruments
				3.1.4 Monitoring and Evaluation
				3.1.5 Critical Dimensions
			3.2 Artificial Intelligence Strategies
				3.2.1 Main Objectives
				3.2.2 Target Stakeholders
				3.2.3 Main Instruments
				3.2.4 Critical Dimensions
		4 Conclusion
		References
	Patents and the Fourth Industrial Revolution: The Global Technology Trends Enabling the Data Economy
		1 What Are the 4IR Technologies?
		Reference
	Comparing the Methodology for the Development and Project Management of Artificial Intelligence Systems
		1 Introduction
			1.1 The Problem
		2 The Three Methodologies
			2.1 Earned Value Management
				2.1.1 JTRS Case: Challenges in the EVM-Only Approach
			2.2 Knowledge Value Added
			2.3 Integrated Risk Management
		3 Comparison of Acquisition Methodologies in AI Development
			3.1 Comparison of Key Attributes
			3.2 Methodologies in AI Acquisition
		4 Conclusions and Recommendations Future Research
		References
Part III: Artificial Intelligence, Productivity and the Next Generation of Competences
	Artificial Intelligence: Productivity Growth and the Transformation of Capitalism
		1 Introduction
		2 Artificial Intelligence and the Speed of Technological Progress
			2.1 Inventions, Product Cycles and Moore´s Law
			2.2 Technological Change, Productivity Growth and Inequality
			2.3 What Accounts for the Slowdown in Productivity Growth?
				2.3.1 Mismeasurement
				2.3.2 Demographic Change
				2.3.3 Lack of Diffusion
			2.4 Innovation and Diffusion: Understanding Two Sides of the Same Coin
			2.5 Intangibles, the Speed of Technological Change and Productivity Growth
		3 AI and the Transformation of Capitalism
			3.1 Transforming Jobs
			3.2 Transforming Workplaces
			3.3 Transforming Societies
			3.4 Blockchain: The Code of Capital
		4 What Is at Stake? Policies for the Digital Age
			4.1 Innovation Policies
			4.2 Taxation and Social Protection
			4.3 Competition Policies
			4.4 Social Wealth Funds
			4.5 Blockchain and the Future of Full Employment
		5 Conclusion: Singularity, Super-Intelligence or Enhanced Collective Intelligence?
		References
	What Artificial Intelligence Can Do and What It Cannot Do
		1 Introduction: A Brief History of Artificial Intelligence
		2 What Is Artificial Intelligence?
		3 How Are AI Programs Built?
		4 Will AI Take over Human Beings?
		5 Conclusion: AI Systems and Human Beings
		References
	AI, Platformization and the Next Generation of Competences
		1 Introduction
			1.1 The value of data
			1.2 Why think in terms of digital assets?
			1.3 Platforms and the new institutional regime
		2 Digitization and Employment
			2.1 The Starting Point: The Impact of Digitization on Employment
			2.2 Change in Work Content and Conditions
		3 Artificial Intelligence and the Shift in Skills Profiles
		4 Competence Structures: A Starting Point
			4.1 The Issue of Competences/Capabilities
		References
Part IV: Artificial Intelligence, Productivity and the Digital Divide
	Are We Pretender of Digitalization?-Towards a New Management Using Telework and Digital Transformation
		1 Introduction
		2 Firm´s Productivity and Digital Transformation
			2.1 Increasing Productivity Through Digitization
			2.2 Using Data to Improve Productivity
		3 Telework and Management
			3.1 Definition of Telework and Its Transition
			3.2 Significance of Introducing Telework
			3.3 Management for Productive Telework
		4 Response of Japanese Companies to Telework in the Corona Disaster: From the ``Emergency Organization Survey´´
			4.1 Overview of the Emergency Organization Survey
			4.2 The Actual Status of Telework Adoption
			4.3 Spread of Communication Tools
				4.3.1 Use of IT systems
		5 Conclusion
			5.1 From Emergency Measures to ``Telework´´ as a Corporate Strategy
			5.2 From Externalized ``IT´´ to Self-Directed ``DX´´
		References
	Real-Time Management: When AI Goes Fast and Flow
		1 Introduction
		2 What Do We Know About Real-Time Management?
			2.1 Real-Time Management Depends on the Managers´ Interpretation of Clock Time
		3 AI and Real Time: Three Scenarios
			3.1 Business as Usual with More Complexity and Just Faster
			3.2 Disruption: Extreme Customization Experience
				3.2.1 Lemonade Home Insurance App AI Going Fast and Flow: Example
			3.3 Distortion: Extreme Dysfunctionality
				3.3.1 Ethical Biases in Programming
				3.3.2 Ethical Biases in Hiring
				3.3.3 Other Ethical Dilemmas
		4 How to Manage and Embrace Real-Time Technologies Such as AI
		Appendix: CTM Survey
			A. Questions About the ``WHAT´´
			B. Questions About the ``WHY´´
			C. Questions About the ``HOW´´
		References
	Artificial Intelligence and the Digital Divide: From an Innovation Perspective
		1 Introduction
		2 The Digital Divide and the Fourth Industrial Revolution: Toward an AI Divide?
		3 Problem Definition and Scoping
		4 The AI Innovation Perspective
			4.1 AI and Patents
			4.2 The Impact of AI: Fields of Application
			4.3 Who Is Part of the AI Revolution? The AI Innovators as Reflected in Patenting Activity
			4.4 AI Researchers as Reflected on Scientific Publications
		5 What Do These Results Tell Us and How Can They Be Explained?
			5.1 AI Is Everywhere
			5.2 Universities Carry Out Research Across Different Geographies and Levels of Economic Development, Yet a Divide Can Be Obser...
			5.3 An AI Divide Seems to Already Be There: But It Remains to Be Seen How the Situation Will Evolve over Time and How AI Will ...
			5.4 The Importance of Data and Related Access
		6 Moving Forward
			6.1 Governments, Policy and Regulation Response
			6.2 Global/Interagency Initiatives: Can AI Lessen the Digital Divide?
			6.3 The Importance of AI Talent
			6.4 Toward AI Democratization-and Reduction of the AI Divide?
		7 Conclusion
Part V: AI, Ethics and the Post-Truth Society
	Post-Truth: Organisational Social Responsibility in an AI-Driven Society
		1 Introduction
		2 Potential Crisis of Truth
		3 Responsibility/Accountability Vacuum of AI Computing
			3.1 Nissenbaum´s Four Barriers to Accountability in Computing
			3.2 Possible Controversial Scenarios
			3.3 Obscured Accountability When FLOSS Is Used
			3.4 Scapegoated End-Users
			3.5 Autonomous Systems as Scapegoats
			3.6 Changes in the Meaning of a Bug
		4 Retrieving Responsibility/Accountability in Computing
			4.1 Expected Roles of Organisations in Terms of Responsible AI
			4.2 Individual Attitudes Required in an AI-Driven Society
			4.3 Remedies for Harm Caused by AI-Based Systems
		5 Conclusions
		References
	Co-constructing Shared Values and Ethical Practice for the Next Generation: Lessons Learned from a Curriculum on Information E...
		1 Introduction
			1.1 A Concrete Need for a Curriculum in Scientific Integrity and Research Ethics for Information Technologists
			1.2 A Fundamental Need to Rethink Our Ethical Frameworks for the Information Age
		2 Constraints and Goals of the Course
			2.1 Training Methods in Research Ethics, Compliance, and Information Ethics
				2.1.1 Scientific Integrity and Research Ethics Training
				2.1.2 Compliance Training in Highly Regulated Industries
				2.1.3 Curricula on Information Ethics, Security, Privacy, and AI Ethics
			2.2 Expectations of Stakeholders
				2.2.1 Faculty
				2.2.2 Students
			2.3 Design Choices
				2.3.1 Cultural Context
				2.3.2 Form
				2.3.3 Toward a Co-construction of Values and Ethical Practices
		3 A Course Proposition
			3.1 The Doctoral Contract
			3.2 Scientific Integrity: Producing Knowledge Correctly
			3.3 Research Ethics: Producing Knowledge Responsibly
			3.4 Information Ethics
				3.4.1 The Object of Study: Computer Systems, Data and Networks
				3.4.2 Society and Economic Players
				3.4.3 The Human
				3.4.4 On the Transformative Nature of Information Technologies
			3.5 Intellectual Property: Protection of the Value Produced
			3.6 Scientific and Ethical Communication on the Internet: Protection and Limits of Free Expression
			3.7 Personal Data and Privacy Online: Protecting Our Digital Identities
			3.8 Emerging Issues (Open Areas of Digital Ethics)
			3.9 Conclusion of the Course
		4 Reception of the Course
			4.1 Feedback from the Students
				4.1.1 Course Appreciation
				4.1.2 Course content
				4.1.3 Didactics
				4.1.4 Questions Difficulty Assessment
			4.2 Feedback from the Teaching Body
		5 Conclusion
			5.1 AI Ethics and Its Place in a General Curriculum on Technology Ethics for STEM Disciplines
				5.1.1 Course Objectives and AI Ethics
				5.1.2 Maturity the Area of AI Ethics
				5.1.3 Socio-centric vs Techno-centric Approaches to Information Ethics
			5.2 Future Directions
		References