Artificial intelligence in application: Legal aspects, application potentials and use scenarios

✍ Scribed by Thomas Barton (editor), Christian Müller (editor)

• Publisher: Springer
• Year: 2024
• Tongue: English
• Leaves: 197
• Edition: 2024
• Category: Library

✦ Synopsis

The book shows application potentials of artificial intelligence in various industries and presents application scenarios on how a practical implementation can take place. The starting point is the description of legal aspects, which includes a European regulation for artificial intelligence and addresses the question of the permissibility of automated decisions.

The description of various application potentials, mostly industry-related, and the presentation of some application scenarios form the focus of the topic volume.

The book is based on the question of how artificial intelligence can be used in entrepreneurial practice. It offers important information that is just as relevant for practitioners as for students and teachers.

This book is a translation of an original German edition. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation.

✦ Table of Contents

Contents
List of Contributors
Part I: Introduction
1: Potential for Artificial Intelligence and Its Application
1.1 Legal Aspects
1.2 Potential Applications
1.3 Operational Scenarios
Part II: Legal Aspects
2: Legal Challenges of Artificial Intelligence and How to Manage Them
2.1 Introduction
2.2 Machine Autonomy as a Legal Challenge: Examples
2.2.1 AI and Copyright Law
2.2.2 Autonomous Driving
2.2.2.1 Legality of the Use of Driving Systems: The StVG Amendment
2.2.2.2 Unpredictability of Autonomous Systems as a Barrier to Innovation?
2.2.2.2.1 Unpredictability as an Inherent Quality of Autonomous Systems
2.2.2.3 Legal Issues of Unpredictability
2.2.2.3.1 Liability Law
2.2.2.3.2 Type-Approval of Autonomous Vehicles
2.2.2.3.3 Vehicle Autonomy Versus Criminal and Administrative Offences Law
2.2.3 Use of AI by Public Authorities
2.2.3.1 Current Situation and Prospects
2.2.3.2 Unpredictability as a Limit to AI Use?
2.2.3.3 Intransparency as a Limit to AI Use?
2.3 Regulatory Perspectives
2.3.1 Requirements on AI Regulation
2.3.1.1 Relevance of Third Party Benefits from AI for Fundamental Rights
2.3.1.2 Use-Specific Risk Assessment and Regulation
2.3.2 Current Regulatory Efforts
References
Part III: Application Potential
3: Application Potential of Artificial Intelligence in the Car Trade
3.1 Introduction
3.2 Challenges for the Automotive Trade
3.2.1 Social Trends
3.2.1.1 Urbanisation
3.2.1.2 Digitisation and Digitalisation
3.2.1.3 Sharing Economy
3.2.2 The Car Trade of the Future
3.3 Business Processes in the Car Trade
3.3.1 Sale of Vehicles
3.3.2 Vehicle Repair and Maintenance
3.3.3 Used Car Purchase
3.4 Artificial Intelligence (AI)
3.4.1 Subfields of Artificial Intelligence
3.4.1.1 Natural Language Processing (NLP)
3.4.1.2 Machine Learning
3.4.2 Economic Impact of Artificial Intelligence
3.5 Use Cases of Artificial Intelligence in the Car Trade
3.5.1 Artificial Intelligence in Vehicle Sales
3.5.2 Artificial Intelligence in Repair and Maintenance
3.5.3 Artificial Intelligence in Used Car Purchasing
3.5.4 Evaluation of Artificial Intelligence Use Cases
3.6 Conclusion
References
4: Acceptance of AI Systems in Retail
4.1 Introduction to the Topic
4.2 Commerce in Transition—From e-Commerce to New Commerce
4.3 Acceptance of Systems and Procedures
4.4 Five Scenarios of AI in Retailing
4.4.1 AI-Based Information Systems
4.4.2 AI-Based Fuzzy Matching
4.4.3 AI-Based Plausibility
4.4.4 AI-Based Trade Initiation
4.4.5 AI-Based Dynamic Pricing
4.5 Assessments and Synopsis by Expert Interviews
4.5.1 Assessment of the Importance and Definition of AI
4.5.2 Assessments of the Detectability of AI Applications
4.5.3 Assessment of Barriers
4.5.4 Assessing the Role of Trust and Ethics
4.5.5 Confirmation of the Five AI Scenarios in the Respective Individual Assessment
4.6 Conclusion and Future Work
4.7 Best Thanks
References
5: Application Potential for Causal Inference in Online Marketing
5.1 Introduction
5.2 Causal Diagrams and Do Operator
5.3 Application Potentials in Online Marketing
5.4 Conclusion
References
6: Influence of Artificial Intelligence on Customer Journeys Using the Example of Intelligent Parking
6.1 How Does Artificial Intelligence Influence Commercial Decisions?
6.2 State of Research on the Influence of AI on Customer Journeys
6.3 Influence of AI on Customer Journeys Using the Example of Intelligent Parking
6.3.1 Identified Patterns Using the Example of Intelligent Parking
6.3.2 Identified Factors Using the Example of Intelligent Parking
6.3.3 Identified Effects Using the Example of Intelligent Parking
6.4 Generalisation and Discussion of the Results
6.4.1 Patterns Are the Most Granular Changes Between Customer Journeys of an AI-Based and Traditional Use Case
6.4.2 Factors Are Derived from Pattern Combinations
6.4.3 Effects Are Derived from Factor Combinations
6.5 Conclusion and Outlook
References
7: Artificial Intelligence (AI) for Platform Business Models
7.1 Platform Economy
7.1.1 Platforms
7.1.2 Network Effects
7.1.3 Business Model Patterns for Platform Companies
7.2 Artificial Intelligence (AI) for Platform Companies
7.2.1 AI Sub-sectors
7.2.2 Types of AI and Intensity of Data Use
7.2.3 AI Deployment Opportunities in Platform Companies
7.3 Importance of AI for Design Aspects of Platform Companies
7.3.1 Hybridisation of Business Models
7.3.2 Design of Suitable Platform Processes
7.3.3 Platform Launch
7.3.4 Monetisation of Platform Services
7.3.5 Determination of a Suitable Degree of Openness
7.3.6 Platform Governance
7.3.7 Key Figures for Platform Controlling
7.4 Recommendations for Action
7.4.1 Further Development into a Platform Company
7.4.2 Participation in the Value Network with Independent Platform
References
Part IV: Operational Scenarios
8: Artificial Intelligence in Automated Document Processing Using the Example of Health Insurance Companies
8.1 Digitisation of Document Processing
8.2 Business Rules
8.3 Rule Processing for Medical Invoices
8.4 Artificial Neural Networks and Deep Learning
8.5 Deep Learning in Text Processing
8.6 Apply AI Methods in an Integrated Way
References
9: AI in Recruiting: Potentials, Status Quo, and Pilot Projects in Germany
9.1 Recruiting as a Field of Application for AI
9.2 Special Requirements for the Use of AI in Recruiting
9.2.1 Solution Orientation
9.2.2 Data Centricity
9.2.3 Data Availability
9.2.4 Decision Transparency and Trustworthiness
9.2.5 Continuity and Robustness
9.3 Limitations for the Use of AI in the Recruiting Process
9.3.1 Acceptance of AI in the Recruiting Process
9.3.2 AI and Compliance with Laws and Standards
9.4 Systematization of Relevant Application Areas of AI in the Recruiting Process
9.4.1 Formulation of Job Advertisements
9.4.2 Publication of Job Advertisements on Job Boards and Social Networks
9.4.3 Structuring and Analysis of Application Data
9.5 Selected Practical Examples of AI in Recruiting
9.5.1 “BetterAds”—Performance Analysis and Augmented Writing of Job Advertisements
9.5.2 “CATS”—Recruiting Chatbots in Applicant Tracking Systems
9.6 Summary and Outlook
References
10: Artificial Intelligence in the Staffing Process: Performance Comparisons of (Un)supervised Learning for the Screening of Job Applications
10.1 Artificial Intelligence in Human Resources Management
10.2 How Artificial Intelligence Could Be Applied in Recruiting and Screening
10.3 The Dataset for Method Piloting and Data Preprocessing
10.4 The Methods Applied of (Un)supervised Learning and Research Hypotheses
10.5 The Results and Discussion
10.6 Outlook
References
11: An AI-Based Framework for Speech and Voice Analytics to Automatically Assess the Quality of Service Conversations
11.1 Introduction
11.1.1 Motivation
11.1.1.1 No Objective Quality Assessment
11.1.1.2 No Explainable Procedures and Characteristics
11.1.2 Interdisciplinary Research Approach
11.2 State of Research
11.2.1 Use of Voice Technology in the Call Centre
11.2.2 Automatic Detection of Paralinguistic Features
11.2.3 Challenges
11.2.3.1 What Measure Defines Good Detection?
11.2.3.2 How Can the Properties to Be Recognized Be Defined and How Can This Definition Be Consistently Mapped to a Corpus?
11.2.3.3 What Is the Influence of Language and Content of the Conversation?
11.3 Design of an Intelligent Assistant to Improve Call Quality
11.3.1 New Approaches to Assessing Call Quality
11.3.2 System Vision
11.3.3 Basic Idea of the Framework
11.4 Experimental Results
11.4.1 Single-Level Classification
11.4.2 Two-Stage Classification
11.5 Conclusion
11.5.1 Outlook: Expert System for Conversation Evaluation
References
Index