Responsible Artificial Intelligence: Challenges for Sustainable Management

Foreword
Artificial and Natural Intelligence Are Converging
Today´s Cybernetics Goes Far beyond this
Two New Laws of Nature for the Great Transformation21
Complexity Is Not Complication
Contents
Artificial Intelligence: Management Challenges and Responsibility
1 Challenges and Prospects of Artificial Intelligence
2 The Impact on Managerial Decision-Making
3 Impact of AI on Corporate Strategy and Organization
4 Management Responsibility and Ethical Implications
References
Artificial Intelligence: Companion to a New Human ``Measure´´?
1 Artificial Intelligence Changes Our Society and Economy
2 Critical Discussions Require New Perspectives
3 Opportunities of Artificial Intelligence in a Sustainable Transformation
4 Further Development of Corporate Social Responsibility
5 Visionary Entrepreneurs Rely on AI Business Models with Positive Impact
References
AI Governance for a Prosperous Future
1 Introduction
2 Artificial Intelligence Is the Quintessence of the Fourth Industrial Revolution
2.1 From Intelligence to Productivity
2.2 The Value of AI
2.3 AI Working for Us
3 Utopia or Dystopia: Where There Is Light, There Is also Shadow
3.1 How to Guide the Emergence of AI
3.2 CSR as Beneficial AI Facilitator
4 All AI Is Not the Same
4.1 From Edge AI to General AI
4.2 The AI Productivity vs. Complexity Paradox
5 Application and CSR Challenges of AI in Companies
5.1 AI Paralysis
5.2 AI Action
5.3 Corporate AI Hierarchies
5.4 AI Roles in the Organization
5.5 From Worker to Trainer and Coach
5.6 AI Collaboration
5.7 Cyber Risks for Cyber Organisms
6 Expanding the CSR Model
6.1 Classic Pyramidal CSR Models
6.2 Expanding the CSR Model
6.3 A Systemic CSR Model
6.4 Cultural Flavours of CSR
6.5 Global Differences in AI Perception
6.6 No Unified Global CSR
7 The Digital Governance Framework
8 Embedding AI Governance in the CSR Model
8.1 Digital and AI Governance: Structure and Transparency
8.2 Data Governance: For Good AI
8.3 Trusted AI: Through Transparency
8.4 Ethical AIs: Lie to Be Loved
8.4.1 Psychological Challenges in the Workplace
8.4.2 Making AI Human or Human-Like?
9 AI Governance
9.1 AI Lifetime Care
9.2 AI Decision Governance
9.3 AI Risk Control
9.4 Dealing with Corrupted AI
9.5 Asimov´s Laws Revisited
9.6 Controlling AIs Through Software Rules
9.7 AI Cybersecurity
10 Artificial Intelligence in the Legal Context
10.1 Ownership Obliges
10.2 Introduction of an `Electronic Person´ as an Opportunity
10.3 Accountability of Electronic Persons: Death and Taxes
10.4 Limits to AI Liability
10.4.1 Accountability and Consciousness
11 CSR as AI Change Enabler
11.1 Cycle of AI Acceptance
11.1.1 Knowledge Is Control
11.1.2 Transparency Creates Confidence
11.1.3 Vision Leads to Engagement
11.1.4 Experience the Benefits
11.1.5 Embrace and Lead Change
12 Outlook
12.1 The Great Resignation
12.2 AI to the Fore
12.3 AI as a Companion
12.4 Closer to AI
12.5 CSR´s Role with AI
Glossary
Governance of Collaborative AI Development Strategies
1 Introduction to Collaborative AI Development
1.1 Relevance of AI Adoption for Companies
1.2 Theoretical Background: Strategic Forms of AI Adoption
1.3 Research Gap for Collaborative AI Development
1.4 Governance of Collaborative AI Development
1.5 Collaboration Opportunities in the AI Development Process
2 Collaboration Opportunities in AI Development
2.1 Opportunities in the Data Preparation Phase
2.2 Opportunities in AI Model Development
2.3 Opportunities in Model Evaluation and Deployment
3 Governance of Risks in Collaborative AI Development
4 Implications, Discussion, and Outlook
4.1 Implications for Practice
4.2 Limitations and Further Research
4.3 Conclusion and Outlook
References
Responsible AI Adoption Through Private-Sector Governance
1 Relevance and Research Gap
2 A Model for Responsible AI Adoption from a Private-Sector Governance Perspective
2.1 AI Adoption as Part of an Organisation´s Innovation Process
2.2 Specifying the Innovation Process Model for AI Adoption
2.3 Integrating Ethics with a Governance Model for Responsible AI Adoption
3 Insights into the Operationalisation of Responsible AI Adoption
3.1 Action Point 1: Creating Ethical Visions
3.2 Action Point 2: Use Case Testing for Long-Term Societal Implications
3.3 Action Point 3: Iteratively Integrating Societal Perspectives
4 Implications, Discussion, and Further Research
References
Mastering Trustful Artificial Intelligence
1 Artificial Intelligence: An Introduction
1.1 Development of AI Research
1.2 AI Made in Austria
1.3 Artificial Intelligence Needs Powerful Hardware
1.4 Forms of Artificial Intelligence: From Rule-Based Systems to Neural Networks
1.5 Machine Learning
1.5.1 Supervised Learning, Training Data, and Ground Truth
1.5.2 Unsupervised Learning
1.5.3 Reinforcement Learning
2 Five AI Challenges
2.1 Modelability
2.1.1 Large Amount of Training Data and Ground Truth
2.1.2 Overfitting and Superstitions
2.1.3 Built-in Backdoors in AI Systems
2.1.4 Summary
2.2 Verifiability
2.2.1 AI Needs New Test Methods
2.2.2 Deceiving AI Systems by Manipulating the Environment
2.2.3 Summary
2.3 Explainability
2.3.1 AI Explanatory Methods
2.3.2 AI in Safety-Critical Systems
2.3.3 Summary
2.4 Ethics and Moral
2.4.1 AI Systems Can Discriminate
2.4.2 Ethical Norms Are Defined by Culture and Societies
2.4.3 EU Guidelines for the Design of AI Systems
2.4.4 Summary
2.5 Responsibility
2.5.1 Summary
3 Social Threat Potential from AI
3.1 Democratization of Technology
3.2 Manipulation of Media
3.2.1 Fake News and Deep Fakes
3.2.2 The Fact Check: A Necessary Tool Support
4 Limits of AI and Diversity of Life
4.1 Singularity: Can AI Surpass Humanity?
4.2 AI Needs a Lot More Intelligence
4.3 Life Is Nonlinear
4.4 Life Is Not Just About Solving Problems
4.5 The Data World of AI Is Not Life
4.6 AI and Morals
5 Conclusions
5.1 Education and Emotional Intelligence to Master the Technology
5.2 Responsibility for the Development of Technology
5.3 AI Needs Standardization
5.4 A Broader Approach to AI Research
References
Technology Serves People: Democratising Analytics and AI in the BMW Production System
1 Digitalisation and Production: A Complex and Dynamic Environment
2 Status Quo
2.1 Quality Work in Production: A Critical Review
2.2 Quality Work: Quo Vadis?
3 CSR in Visual Analytics and Artificial Intelligence
3.1 How Does the Use of Data Analytics and AI Change Corporate Responsibility?
3.2 How Does the BMW Group Deal with the Consequences and Possibilities of AI? How Are the Potential Risks Dealt with, and Wha...
3.3 What Does AI Mean for the Company´s (Global) Value Creation and Strategy and How Does It Change the Company´s Social Respo...
3.4 Which Cooperation Is Required and How Are the Different Approaches to Responsibility and Sustainability Dealt with?
3.5 What Challenges Do Data Analytics and Artificial Intelligence Pose for Managers at All Levels in Production?
4 Conclusion
References
Sustainability and Artificial Intelligence in the Context of a Corporate Startup Program
1 TechBoost, a Startup Program Designed to Drive Sustainability Through Innovation in an B2B Environment
2 Flip App: Sustainability in Collaboration Using a Messenger App
2.1 How Can the Flip App Drive Sustainability with Digitization and Artificial Intelligence
2.2 What Kind of Ethical Principles Has Flip Adapted into Their Software Development
2.3 How Does the Partnership with a Corporate Supports the Sustainability Strategy of Flip
2.4 Future Developments at Flip App
3 rooom.com: How the Metaverse Is Driving Sustainability with Digitization and AI
3.1 How the rooom Software Supports Sustainable Principles
3.2 Sustainability and Responsibility in the Metaverse
3.3 Virtual Events in the Metaverse
4 Outlook
Exploring AI with Purpose
Developing Responsible AI Business Model
1 Setting the Context
2 Understanding the Current Ecosystem of Responsible AI
2.1 Regulatory Ecosystem
2.2 Research Ecosystem
2.3 Business Ecosystem
3 Stages of Responsible AI Maturity
4 Responsible AI Business Model
4.1 Principles
4.2 Pillars
4.3 Business Model
4.3.1 Responsible AI Business Model Canvas
4.3.2 Responsible AI Decision-Making Canvas
4.4 Steps Toward Responsible AI Business
4.4.1 Step 1: Understanding RAI Landscape
4.4.2 Step 2: Assessing Current Gaps in AI Lifecycle
4.4.3 Step 3: Establishing Business Value of RAI
4.4.4 Step 4: Developing a Framework
4.4.5 Step 5: Aligning Principles to Framework
4.4.6 Step 6: Structuring Actionable Plan for RAI
4.4.7 Step 7: Integrating Skills for RAI
4.4.8 Step 8: Putting RAI in Practice
5 Convergence of Social Responsibility
ESG Fingerprint: How Big Data and Artificial Intelligence Can Support Investors, Companies, and Stakeholders?
1 Status Quo
2 Introduction ESG Risk Management and Information Systems
3 Concept for the Development of a Taxonomy for the Classification of ESG-Relevant Opportunities and Risks
3.1 Structure of the Case Base (Empirical Data Basis)
3.2 Analysis and Evaluation
3.3 Iteration 1: Conceptual Development (from Concept to Empiricism)
3.4 Iteration 2: Empirical Development (from Empiricism to Concept)
3.5 Iteration 3: Empirical Evaluation (from Empirical to Conceptual)
4 Application of the Concept to Develop an ESG Fingerprint for AI-Based Information Systems
4.1 Case Study 1: Air and Water Pollution (E)
4.2 Case Study 2: Child Labor in the Supply Chain (S)
4.3 Case Study 3: Corruption (C)
4.4 Application of the Taxonomy to Case Studies for ESG Fingerprint Development
4.5 Potentials for the Use of Big Data and Artificial Intelligence
5 Summary and Outlook
It´s Only a Bot! How Adversarial Chatbots can be a Vehicle to Teach Responsible AI
1 Introduction
2 Background
2.1 Exposing CS Students to AI Ethics and Responsible Innovation
2.2 Teaching Resources for Responsible AI
3 Exploring Disruptive Technologies Course
3.1 Pedagogical Goals
3.2 Course Format
3.3 Inputs and Assignments
3.4 Student Project
4 Outcome
4.1 Student Projects
4.2 Guidelines
5 Reflection
5.1 Student Perspective
5.2 Teacher Perspective
6 Conclusion
References
Concerted Actions to Integrate Corporate Social Responsibility with AI in Business: Two Recommendations on Leadership and Publ...
1 Introduction
2 Setting the Scene: CSR, Ethics and SDGs
3 A Recommendation on Business Leadership: Adopting a Three-Level Mindset Framework
3.1 Contextualising the Framework: A Case Study of Four AI4SDGs Projects in Latin America
3.2 The Application of the Three-Level Mindset Framework in Different Sectors and Its Limitations
4 A Recommendation on Public Policy: AI Regulation and Policy Harmonisation
4.1 The Experience of Four AI4SDGs Projects in Latin America: Regional Fragmentation of AI Policies and Regulations
4.2 Identification of a Forum for Policy Harmonisation and Limitations
5 Conclusion
References
AI and Leadership: Automation and the Change of Management Tasks and Processes
1 The Combination of Artificial and Human Intelligence
2 Leadership with AI: Why There Is No Alternative
3 The Optimum and Pace of Development
4 Leadership Encompasses Implementation Strength
4.1 Recognising AI Potential and Finding Solutions
4.2 Success Factors for the Implementation of AI Systems
4.3 Institutionalising and Holisting Implementation
5 Case Study: AI for Continuous Monitoring of a Company´s Business Environment
6 Conclusion
Achieving CSR with Artificially Intelligent Nudging
1 Introduction
2 The Emergence of Human-Agent Collectives
3 Homo Economicus and Machina Economica
4 A Different Way of Thinking Complements
5 Augmented Human-Centered Management
6 Augmentation with Digital Nudging
7 Nudges for CSR
8 Conclusion
References