COURSE UNIT TITLE

: INTRODUCTION TO AI AND COMPUTATIONAL SOCIAL SCIENCE

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
UIR 4509 INTRODUCTION TO AI AND COMPUTATIONAL SOCIAL SCIENCE ELECTIVE 3 0 0 6

Offered By

Political Science and International Relations (English) (UOLP-New York State University (Suny Albany))

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSISTANT PROFESSOR ILKIM ÖZDIKMENLI ÇELIKOĞLU

Offered to

Political Science and International Relations (English) (UOLP-New York State University (Suny Albany))

Course Objective

This course examines the complex relationship between artificial intelligence and social science. It begins by establishing the foundational principles of AI, including its operational mechanisms and developmental drivers, to illuminate its origins and expanding influence. Subsequently, the course investigates AI's pervasive impact on politics and the public sphere, drawing upon relevant theories, research methodologies, and historical contexts. Through the analysis of contemporary international case studies and expert reports, students will assess the dynamic interplay of digitalization, society, and politics. This assessment aims to delineate both the potential and limitations of AI in shaping future societal structures. This course has no prerequisites.

Learning Outcomes of the Course Unit

1   Students will understand and articulate the fundamental concepts of AI, computational social science and their historical development.
2   Students will gain a comprehensive understanding of diverse arguments in social sciences and STEM fields regarding the role of artificial intelligence within society.
3   Students will develop the capacity to critically evaluate the use of AI and computational social science applications in social science research.
4   Students will develop an understanding of the basic principles and applications of computational methods, such as NLP, SNA, and machine learning, in the analysis of political texts, social networks, and public opinion.
5   Students will analyze and evaluate the ethical dimensions, challenges, and opportunities of AI governance, policy analysis, development, and deployment, including issues of bias, privacy, and social justice.
6   Students increase their writing skills and English proficiency by preparing and delivering well articulated presentations and term papers.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 1. Introduction: Mapping the terrain Mixing oil with water What is computational social science and how does it differ from traditional approaches Overview of AI: key concepts and historical development The impact of digital data on political research
2 2. Digital Data, Politics and Society: What is at stake The nature of digital data: an overview of social media, online platforms, government databases. Advantages and limits of utilizing digital data in social science research Case studies on elections, public opinion, and social movements.
3 3. Text Analysis and Natural Language Processing (NLP) Concepts: What magic is this Understanding the concept of "text as data." Introduction to NLP concepts: sentiment analysis, topic modeling, and discourse analysis. Case studies: Analyzing political speeches, social media posts, and news articles.
4 4. Social Network Analysis (SNA) Concepts: the future of social capital Understanding network concepts: nodes, edges, centrality, and community detection. How SNA can be used to study political networks and influence. Case studies: Analyzing political communication networks and social media echo chambers.
5 5. Machine Learning and Political Analysis: Concepts and possible pitfalls Introduction to machine learning concepts: supervised and unsupervised learning. Applications of machine learning in social sciences (e.g., political forecasting, behavior analysis) Case studies: Predicting election outcomes, identifying political trends.
6 6. Analyzing Public Opinion and Political Communication in the Digital Age: Opening the Pandora s Box The challenges of measuring public opinion in the digital age Analyzing political discourse on social media and online platforms. The spread of misinformation and propaganda.
7 7. Review Week
8 8. Computational Modeling and Simulation: Concepts and Some Applications Introduction to agent-based modeling and simulation. Understanding how computational models can be used to simulate political processes. Case studies: Simulating the spread of political ideas and the dynamics of social movements.
9 9. Policy Analysis and Governance: A Conceptual Overview The potential of AI for policy evaluation and design. Analyzing government data and public policy. Case studies: Using computational methods to evaluate policy effectiveness.
10 10. AI Governance: Putting the Genie Back Into the Bottle The current landscape of AI governance: state vs. non-state actors. An overview of existing governance frameworks and models
11 11. AI Governance: The Leviathan vs The Utopia Current attempts and cases to regulate the development and use of AI across the globe
12 12. Ethical Considerations and Bias in AI and Computational Social Science Does AI have gender Algorithmic bias and fairness in computational applications. Privacy and data security in the digital age. The impact of AI on social justice.
13 13. The Future of AI and Computational Social Science: the power of interdisciplinary research Emerging trends and challenges. The role of computational methods in shaping the future of sciences and societies
14 14. Concluding remarks Round Table Discussion

Recomended or Required Reading

The History of Artificial Intelligence, https://sites.harvard.edu/sitn/2017/08/28/historyartificial-intelligence/
Matthew Salganik, Bit by Bit: Social Research in the Digital Age, Princeton: Princeton University Press, 2018.
Brian Christian, The alignment problem: Machine learning and human values, W.W.Norton.
Ethan Mollick, Co-intelligence: Living and working with AI. Portfolio/Penguin.
Dan Jurafsky and James H. Martin, Speech and Language Processing (3rd ed. draft) https://web.stanford.edu/~jurafsky/slp3/
John McLevey, Doing Computational Social Science: A Practical Introduction, Los Angeles: Sage, 2022.

Please note that since the course subject involves a fast-paced technology, all course readings are subject to change, and will be updated/adjusted as needed.

Planned Learning Activities and Teaching Methods

1. Lecture
2. Participation
3. Assignment
4. Presentation
5. Exam

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MT Midterm
2 TP TermProject
3 PRS Presentation
4 FN Final
5 FCG FINAL COURSE GRADE MT * 0.30 + TP * 0.20 + PRS * 0.10 +FN * 0.40
6 RST RESIT
7 FCGR FINAL COURSE GRADE (RESIT) MT * 0.30 + TP * 0.20 + PRS * 0.10 + RST * 0.40


Further Notes About Assessment Methods

None

Assessment Criteria

1. The learner will accurately identify and explain the fundamental concepts of Artificial Intelligence (AI) and computational social science as well as their historical developmentk key milestones and influences.
2. The learner will analyze and compare different perspectives on AI's societal impact, identifying key areas of agreement and disagreement.
3. The learner will assess the strengths and limitations of specific AI applications, considering their methodological and ethical implications.
4. The learner will demonstrate an understanding of how Natural Language Processing (NLP), Social Network Analysis (SNA), and machine learning are used to analyze political texts, social networks, and public opinion.
5. The learner will critically discuss issues of bias, privacy, and social justice related to AI.
6. The learner will effectively communicate complex ideas related to AI and computational social science in both written and oral formats.

Language of Instruction

English

Course Policies and Rules

1. Attending at least 70% of lectures is mandatory.
2. Plagiarism of any type will result in disciplinary action.
3. Students may not collaborate in any of the assignments or presentations unless explicitly instructed by the lecturer.

Contact Details for the Lecturer(s)

Prof. Dr. Gül M. Kurtoğlu Eskişar, gul.kurtoglu[at]deu.edu.tr

Office Hours

By appointment

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparations before/after weekly lectures 14 3 42
Preparation for midterm exam 1 10 10
Preparation for final exam 1 10 10
Preparing presentations 1 10 10
Preparing assignments 1 25 25
Midterm 1 1,5 2
Final 1 1,5 2
TOTAL WORKLOAD (hours) 143

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13
LO.15
LO.25
LO.35
LO.45
LO.55
LO.65

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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