AI Agents Simulations as a Method for Science, Technology and Society studies

בינה מלאכותית במחקר: מתודה פרקטיקה וביקורת

   AI Agents Simulations as a Method for Science, Technology and Society studies

Denisa Reshef Kera| STS
278128-01

 

Course description and learning goals

Course Abstract

This course explores the design, development, and application of AI agents with a focus on Science, Technology, and Society (STS). As AI technologies increasingly shape public discourse, policymaking, and societal decision-making, this course investigates how AI agent simulations can be used for public engagement, experimental regulatory sandboxes, and policy prototyping.

Designed for students without prior technical experience, this practice-based course introduces foundational AI concepts, prompt engineering, AI agent design, and multi-agent simulations. Students will learn to prototype and deploy AI agents using tools such as OpenRouter, LangChain, and Streamlit, and explore AI-to-AI interactions to simulate real-world decision-making and regulatory challenges. Through these simulations, students will critically assess AI’s role in governance, automation, and knowledge production, developing experimental approaches to AI regulation and public engagement. By the end of the course, students will have built their own AI agent simulations, examined AI's influence on society, and developed strategies for using AI as a tool for public discourse and policy exploration.

Learning objectives

Knowledge

1. Learners will describe the fundamental concepts of AI agents, including tokenization, embeddings, model parameters, and prompt engineering techniques.

2. Learners will define key AI-related terms and concepts such as AI, NLP, ML, neural networks, deep learning, generative AI, agentic AI, and multi-agent systems.

3. Learners will write effective AI prompts, system instructions, and API calls to shape AI agent behavior and interactions.

Skills 

1. Learners will analyze AI agent simulations to explore their impact on public engagement, regulatory experimentation, and decision-making in Science, Technology, and Society (STS).

2. Learners will evaluate the ethical, societal, and regulatory implications of AI agents, identifying potential biases, risks, and strategies for responsible deployment.

3. Learners will design and prototype AI agents that integrate external APIs, multimodal inputs (text, voice, images), and AI-to-AI interactions for experimental simulations in public engagement and policy testing.

Lessons plan (Including active learning): 

Final grade

Course requirements

Students are expected to participate actively in weekly sessions, engage in structured peer feedback, and complete hands-on assignments related to AI prompt engineering and agent design. They will complete a series of prompt engineering exercises focused on iterative improvements of AI outputs and critically reflect on model behavior and interaction design. The central assignment consists of developing a prototype AI agent for public engagement, accompanied by a written reflection on the design process and societal implications. Students will present their work and provide peer critique in the final weeks. Regular attendance, participation in collaborative activities, and timely submission of all assigned tasks are required.

Prerequisites

Bibliography: Up-to-date reading, viewing, and listening content items

Foundational Texts

- Binns, R. (2018). Algorithmic Accountability and Public Reason. “Philosophy & Technology, 31”(4), 543–556. https://doi.org/10.1007/s13347-017-0263-5  

- Crawford, Kate. “Atlas of AI: The Real Worlds of Artificial Intelligence.” London: Yale University Press, 2021.  

- Danaher, J., Hogan, M. J., Noone, C., Kennedy, R., Behan, A., De Paor, A., et al. (2017). Algorithmic governance: Developing a research agenda through the power of collective intelligence. “Big Data & Society, 4”(2), 2053951717726554. https://doi.org/10.1177/2053951717726554  

- Daston, Lorraine. “Rules: A Short History of What We Live By.” Princeton University Press, 2022.  

- Floridi, L., & Cowls, J. (2019). A Unified Framework of Five Principles for AI in Society. “Harvard Data Science Review, 1”(1). https://doi.org/10.1162/99608f92.8cd550d1  

- Gillespie, T. (2014). The Relevance of Algorithms. In: Gillespie, T., Boczkowski, P. J., & Foot, K. A. (Eds.) “Media Technologies: Essays on Communication, Materiality, and Society.” Cambridge: MIT Press, pp. 167–194. https://www.microsoft.com/en-us/research/wp-content/uploads/2014/01/Gil…  

- Hildebrandt, M. (2020). Code Driven Law: Scaling the Past and Freezing the Future. “SSRN Scholarly Paper No. 3522079.” https://doi.org/10.2139/ssrn.3522079  

- Issar, S., & Aneesh, A. (2021). What is Algorithmic Governance? “Sociology Compass.” https://doi.org/10.1111/soc4.12955  

- Kitchin, R. (2014). Thinking Critically About and Researching Algorithms. “The Programmable City Working Paper 5.” Available at SSRN: https://ssrn.com/abstract=2515786  

- Latzer, M., & Festic, N. (2019). A guideline for understanding and measuring algorithmic governance in everyday life. “Internet Policy Review, 8”(2). https://policyreview.info/articles/analysis/guideline-understanding-and…  

- Mattu, J. A., Larson, J., Kirchner, L., & Surya. “Machine Bias.” ProPublica. Retrieved August 23, 2022, from https://www.propublica.org/article/machine-bias-risk-assessments-in-cri…  

- Medina, E. (2015). Rethinking Algorithmic Regulation. “Kybernetes, 44”(6/7), 1005–1019. https://doi.org/10.1108/K-02-2015-0052  

- Reijers, W., Orgad, L., & de Filippi, P. (2022). The Rise of Cybernetic Citizenship. “Citizenship Studies, 0”(0), 1–20. https://doi.org/10.1080/13621025.2022.2077567  

- Rouvroy, A. (2020). Algorithmic Governmentality and the Death of Politics. “Green European Journal.” https://www.academia.edu/44097966/Algorithmic_Governmentality_and_the_D…  

- Sætra, H. S. (2020). A Shallow Defence of a Technocracy of Artificial Intelligence: Examining the Political Harms of Algorithmic Governance. “Technology in Society, 62.” https://doi.org/10.1016/j.techsoc.2020.101283  

- Yeung, K. (2018). Algorithmic Regulation: A Critical Interrogation. “Regulation and Governance, 12”(4), 505–523. https://doi.org/10.1111/rego.12158  

- Ziewitz, M. (2016). Special Issue Introduction: Governing Algorithms: Myth, Mess, and Methods. “Science, Technology, & Human Values, 41”(1), 3–16. https://www.jstor.org/stable/43671280  

Other Recommended Readings

- O’Neil, C. “Weapons Of Math Destruction: How Big Data Increases Inequality and Threatens Democracy.” New York: Crown, 2016.  

- Stephens-Davidowitz, S. “Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are.” 2017.  

- Colakides, Y., et al. “State Machines: Reflections and Actions at the Edge of Digital Citizenship, Finance, and Art.” Amsterdam: Institute of Network Cultures, 2019.  

- Ebers, M., & Gamito, M. C. “Algorithmic Governance and Governance of Algorithms: Legal and Ethical Challenges.” Springer International Publishing, 2020.  

- Rieder, B. (2020). “Engines of Order: A Mechanology of Algorithmic Techniques.” Amsterdam University Press. https://doi.org/10.2307/j.ctv12sdvf1  

- “The Algorithmic Society: Technology, Power, and Knowledge.” 2021. Routledge Studies in Crime, Security, and Justice. London/New York: Routledge. https://doi.org/10.4324/9780429261404  

- “The Oxford Handbook of Governance.” (2012). https://doi.org/10.1093/oxfordhb/9780199560530.001.0001