Bachelorarbeit, 2018
35 Seiten
1 Introduction
2 Methodology
2.1 Theoretical Approach
2.1.1 Countervailing Eects
2.1.2 Impeding Eects
2.1.3 The Basic Formal Model
2.1.4 Technology and Labor Demand
2.1.5 Discussion
2.2 Empirical Approach
2.2.1 Methodology and Data Sources
2.2.2 Results
2.2.3 Discussion
3 Policymaking
3.1 The U.S. approach
3.2 Policy Responses to AI and its Eect on Employment and Wages
3.3 Discussion
4 Further novel research
5 Conclusion
6 Appendix
6.1 Table and Figures
This thesis examines the economic and societal impacts of artificial intelligence and automation, specifically focusing on the displacement of labor and the emergence of countervailing effects that may balance these disruptions. The research aims to evaluate current U.S. policy responses and proposes a framework for navigating the transition towards an AI-integrated economy.
1 Introduction
AI has been a buzzword since its beginning in the late 1950s. However, the recent robust and developing focus on four self-enforcing trends take the notion of disruptive technologies onto a higher level. These are statistical and probabilistic methods, the abundance of increasingly large amounts of data, the accessibility of cheap, enormous computational power, and the transformation and adaption of more places into IT-friendly environments (e.g., Smart Cities and IoT).
These fundamental elements of AI can be found in many applications. Boston Consulting Group (2017) states some of these applications as follows: Marketing and Sales with personalized services and goods, Research and Development with aggressive forms of data collection and automating previously outsourced service tasks in large companies by combining AI with robotic processing automation.
AI’s unprecedented ubiquity among these areas and more has underlined the feasibility, importance, and scalability of AI. Consequently, critical voices raised about how this enormous disruptive technology should be regulated and adjusted into the economy. As a response to the ethical, social and economic impact of AI, in October 2016, the White House Office of Science and Technology Policy (OSTP), the European Parliament’s Committee on Legal Affairs, and in the UK, the House of Commons’ Science and Technology Committee released their initial reports on how to prepare for the future of AI.
1 Introduction: Provides an overview of the AI landscape, its disruptive potential, and the motivation for conducting a distinguished analysis of its societal impacts.
2 Methodology: Summarizes theoretical and empirical frameworks regarding how AI and automation affect employment, including a task-based approach and analysis of industrial robots.
3 Policymaking: Reviews and compares policy approaches from the U.S., EU, and UK, with a specific focus on U.S. strategies for managing AI-driven labor market disruptions.
4 Further novel research: Identifies critical areas for future investigation, including the intersection of inequality, skill mismatches, and policy efficacy.
5 Conclusion: Synthesizes the main findings, reiterating that while displacement effects are real, they can be counterbalanced by proper policy, education, and institutional adjustments.
6 Appendix: Presents supporting tables and figures illustrating robot usage, employment trends, and public expenditure data.
Artificial Intelligence, Automation, Labor Demand, Employment Polarization, Productivity Effect, Displacement Effect, Reinstatement Effect, Industrial Robots, Policymaking, Wage Inequality, Human Capital, Skill Mismatch, Labor Market, Economic Policy, Technological Change.
The thesis explores the tension between AI-driven job displacement and the countervailing economic forces that create new labor demands, arguing that a task-based framework is essential for understanding these dynamics.
The work centers on the economic impact of automation, the role of governmental regulation, the importance of education and retraining, and the balance between innovation and social protection.
The goal is to provide a comprehensive framework to understand how AI affects society and to evaluate the effectiveness of current policies in ensuring that AI contributes to shared prosperity.
The author uses a synthesis of theoretical economic models (based on the work of Acemoglu and Restrepo) and an empirical review of existing studies and data sets regarding industrial robot usage in U.S. and European labor markets.
The main section details the mechanics of automation (productivity and displacement effects), performs an empirical analysis of robot-induced labor changes, and critiques policymaking approaches regarding AI.
Key terms include Artificial Intelligence, Automation, Labor Demand, Employment Polarization, Productivity Effect, and Displacement Effect.
High-productivity technologies often trigger positive countervailing effects (like increased production and real income), whereas "so-so" technologies are only productive enough to displace human labor without generating sufficient offsetting benefits.
The author argues that UBI might act as a "lean-back" solution that gives up on the goal of reinstating the displaced workforce, potentially increasing long-term social inequality and reducing the prospects for future generations.
The author suggests the government should act as a lead social planner, fostering a framework that balances innovation with public education, rigorous retraining programs, and regulations that protect worker bargaining power.
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