This thesis provides a framework to think about AI and how it affects and shapes the society. Finally, this thesis is structured as follows. First I will start summarizing the Theoretical and Empirical Approaches by Acemoglu and Restrepo in stating the effects of Automation on wages and employment. Afterward, I will state Cath et al.’s review of recent policymaking decisions regarding AI and how to “make a Good AI Society” by the U.S., EU and UK. Furthermore, after each section, I am going to discuss each paper respectively and supplement them with my findings. Finally, I will state further novel research which could foster implementing a society in which AI does more help than harm and conclude afterward.
Table of Contents
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
Research Objectives and Topics
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.
- The theoretical impact of AI and automation on employment and wages.
- Empirical evidence of robot usage and its consequences for labor markets.
- Evaluation of governmental policy approaches to mitigate negative AI effects.
- Strategies for fostering education, training, and resilient labor markets in the age of AI.
Excerpt from the Book
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.
Chapter Summary
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.
Keywords
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.
Frequently Asked Questions
What is the fundamental premise of this thesis?
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.
What are the central themes discussed in this work?
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.
What is the primary research goal?
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.
Which scientific methods are employed?
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.
What topics are covered in the main section?
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.
Which keywords best characterize the paper?
Key terms include Artificial Intelligence, Automation, Labor Demand, Employment Polarization, Productivity Effect, and Displacement Effect.
How do "so-so" technologies impact the labor market differently than high-productivity technologies?
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.
Why does the author argue that Universal Basic Income (UBI) might be a problematic response to AI?
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.
What role should the government play in the development of a "good AI society"?
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.
- Quote paper
- Oguzhan Bekar (Author), 2018, Artificial Intelligence. Benefits, Risks and Effects on Society, Munich, GRIN Verlag, https://www.grin.com/document/459581
