Artificial Intelligence’s Reproduction of Big Data-Based Race and Ethnicity Discrimination: An Evaluation via Google Search Engine

İÇİNDEKİLER

1. INTRODUCTION

2. THEORETICAL FRAMEWORK OF BIG DATA AND ARTIFICIAL INTELLIGENCE

2.1. Big Data, Artificial Intelligence and Machine Learning

2.1.1. The Concept of Big Data and its Elements

(Understanding Big Data – From Information to Data)

2.1.2. Artificial Intelligence and its History (Digital Brain Born-Artificial Intelligence and Artificial Intelligence Networks)

2.1.2.1. Structural Model of Artificial Neural Networks

2.1.2.2. Artificial Neural Networks by Structural Characteristics

2.1.2.3. Fundamental Elements of Artificial Neural Networks

2.1.3. Machine Learning

2.1.3.1. Deep Learning

2.1.3.2. Natural Language Processing

2.1.4. The Relationship between Big Data, Artificial Intelligence and Machine Learning

2.2. Mass Media and Artificial Intelligence Systems in Digital Transformation

2.2.1. Mass Media, Big Data, and Artificial Intelligence

2.2.2. Google Search Engine: The Relationship with Big Data and Artificial

Intelligence Systems

2.2.2.1. The Operating Principle of a Search Engine

2.2.2.2. Google’s Business Case with Big Data

3. DIGITAL SURVEILLANCE AND ETHICS

3.1. Surveillance and Digital Surveillance

3.1.1. Conceptual Framework

3.1.2. From Panopticon to Banopticon

3.1.3. Big Data Surveillance: Google’s Superpanopticon

3.1.4. The (Ethical) Challenges of Digital Surveillance with Big Data

3.2. Ethics: A Comparative Study

3.2.1. Conceptual Framework

3.2.2. Ethical Approaches: Normative Theories

3.2.2.1. Teleological ethics (Consequentialism)

3.2.2.2. Deontological Ethics (Duty Ethics)

3.2.2.3. Virtue Ethics (Character-Based Ethics)

3.2.3. Universal Morality and Social Structure: Perspectives of Kant and Marx

3.2.3.1. Universal Moral Perspective from Kantian Point of View

3.2.3.2. Historical aMaterialism Approach from Marxist Point of View and

Ideology as the Visibility of Reality

3.2.3.3. Marx's Conception of Freedom: A Perspective Different from Kant’s Paradigm

3.2.3.4. Marx’s Materialist Ethics Against Kant’s Universalist Ethics

3.3. The Responsibility of Artificial Intelligence for the

Ethical Challenges of Big Data

4. THE PROBLEM OF RACIAL AND ETHNIC DISCRIMINATION

IN ARTIFICIAL INTELLIGENCE

4.1. The Concept of Discrimination

4.1.1. Race and Ethnicity: The Conceptual Framework and its Role Throughout Discrimination

4.1.2. Racial and Ethnic Discrimination from Kantian and Marxist Perspectives

4.1.3. Migration and Immigrants

4.2. How Does Artificial Intelligence Reproduce Racial and Ethnic Discrimination?

4.2.1. The Power of Big Data

4.2.2. Interconnected Challenges arising from Big Data: Human Bias,

Superpanopticon, ‘Content Relevance’ and ‘Linking’ Algorithm and

Geopolitical Impact

5. ANALYSIS OF THE IMAGES OF ‘GÖÇMEN’ AND ‘IMMIGRANT’ AND

THE TEXTS HOSTING THESE IMAGES

5.1. Research Design

5.1.1. Clustering and Classification Machine Learning Analysis

5.1.2. Social Semiotics Analysis and Four-Step Photo Reading

5.1.3. Van Dijk’s Socio-Cognitive Critical Discourse Analysis

5.2. Methodology

6. FINDINGS

7. DISCUSSION AND CONCLUSION

REFERENCES

APPENDIX