Bachelorarbeit, 2024
58 Seiten, Note: 1,0
1. Introduction
1.1 Goals of the Project and Research Question
1.2 Parameters of Project and State of Current Research
1.3 Overview and Methodology
2. Introduction to Artificial Intelligence
2.1 Artificial Intelligence
2.1.1 Machine Learning
2.1.2 Deep Learning
2.1.3 Robotics
2.1.4 Natural Language Processing
2.1.5 Generative AI
2.2 Historical Development of Artificial Intelligence
3. Economic Effects of Artificial Intelligence on the Labor Market
3.1 Automation and Potential Job Losses
3.2 AI as a Support for Growth
3.3 Emergence of New Occupational Fields
4. Skills Required in the Age of AI
4.1 Traditional Skills and their Relevance
4.2 Emerging Skills and their Relevance
4.3 Reskilling and Upskilling
5. Analysis of AI's Impact and Skills Requirements in Relevant Sectors
5.1 Finance
5.1.1 Application of AI in Finance
5.1.2 Resulting Skill Requirements in Finance
5.2 Public Administration
5.2.1 Application of AI in Public Administration
5.2.2 Resulting Skill Requirements in Public Administration
6. Conclusion
This thesis investigates the transformative impact of Artificial Intelligence on the labor market, specifically focusing on how AI-driven technological changes alter job requirements and professional skill profiles. The primary research questions evaluate the nature of this impact on job loss versus creation, and identify the specific competencies necessary for the future workforce.
2.1.1 Machine Learning
The approach in computer science differs from the past approach to programming machines to today's present concept: how to develop machines to enable them to learn. Therefore, machine learning is concerned with the primary notion of how, on the basis of large data sets, algorithms learn and improve themselves in order to develop models for the future. For this reason, ML is not only an interface of computer science and statistics, but the extraction of knowledge also takes place in the fields of psychology or neuroscience (Mitchell, 2006). How machine learning efficiently learns from the datasets describes the functions of descriptive, predictive, and prescriptive. Descriptive machine learning involves a system using data to clarify what has occurred, whereas predictive involves what will occur, and prescriptive consists of a system using the data to make recommendations about what actions to take (Malone, Rus and Laubacher, 2020). Furthermore, ML differentiates between three primary paradigms: supervised, unsupervised, and reinforcement training applications.
1. Introduction: This chapter provides the foundation for the thesis, outlining the emergence of AI and defining the core research questions concerning labor market impacts and required skills.
2. Introduction to Artificial Intelligence: Provides a comprehensive definition of AI and its key sub-disciplines, including machine learning, deep learning, and generative AI, alongside its historical background.
3. Economic Effects of Artificial Intelligence on the Labor Market: Analyzes the interplay between automation, potential job loss, overall economic growth, and the creation of new professional occupations.
4. Skills Required in the Age of AI: Examines the shifting skill landscape, emphasizing the increasing importance of human-centric skills and the necessity of strategic reskilling programs.
5. Analysis of AI's Impact and Skills Requirements in Relevant Sectors: Investigates the practical integration of AI in finance and public administration, detailing how specific task changes influence necessary professional qualifications.
6. Conclusion: Synthesizes the findings to answer the research questions, reflecting on the long-term transformation of the global labor market driven by AI technologies.
Artificial Intelligence, Machine Learning, Deep Learning, Generative AI, Natural Language Processing, Robotics, Automation, Labor Market, Skill Requirements, Reskilling, Upskilling, Financial Sector, Public Administration, Digital Literacy, Human-Machine Interaction
The thesis explores the impact of Artificial Intelligence on the modern labor market, specifically focusing on how AI technologies influence employment dynamics and the specific skills workers need to remain competitive.
The core themes include the definition of AI technologies, the economic consequences of automation, the changing demand for professional qualifications, and case studies within the finance and public sectors.
The primary aim is to analyze how AI integration impacts the labor market and whether it acts primarily as a replacement for human labor or a catalyst for new skill requirements and occupational opportunities.
The work is conducted as a literature-based study, utilizing primary and secondary sources, research reports, and industry-specific use cases to analyze current developments.
The main body breaks down AI sub-disciplines, evaluates the economic waves of automation (including historical context), discusses the necessity of reskilling programs, and provides sector-specific analysis of finance and public administration.
Key terms include AI integration, labor market automation, reskilling, upskilling, and sector-specific skill demands in finance and administration.
In finance, AI is increasingly used for compliance, fraud detection, and algorithmic trading, which simplifies efficiency but requires specialists to transition from traditional accounting skills to data interpretation and human-machine interaction skills.
The author concludes that while AI technologies trigger structural shifts and job displacement, they simultaneously catalyze new economic growth and demand for new skills, framing AI as an essential, persistent factor in the future of work.
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