Dr. Martin Neumann

TISSS Lab Summer School: Artificial Intelligence, Simulation and Society

Instructors: Prof. Dr. Petra Ahrweiler; Blanca Luque Capellas; Martin Neumann
Shortname: S Techniksoziologie
Course No.: 02.149.16911
Course Type: Seminar

Requirements / organisational issues

Zielgruppe:

Bachelor Studierende im Studiengang Soziologie (Kernfach) [po 2011, 2016]
Bachelor Studierende im Studiengang Soziologie (Beifach) [po 2016]
Bachelor Studierende im Studiengang Wirtschaftspädagogik (Schwerpunktfach "Sozialwissenschaften")

Stellung im Studiengang:

B.A. Soziologie Kernfach: Modul "Wahlveranstaltung Soziologie (Vertiefung)" [po 2011]
B.A. Soziologie Kernfach: Vertiefungsveranstaltung im Modul "Vertiefungs- und Wahlveranstaltungen 1" [po 2016]

bzw.

B.A. Soziologie Kernfach: Vertiefungsveranstaltung im Modul "Vertiefungs- und Wahlveranstaltungen 2" [po 2016]
B.Sc. Wirtschaftspädagogik Schwerpunktfach "Sozialwissenschaften": Modul "Aufbaumodul Soziologie"

Master's students who would like to participate in this seminar as part of Module 2 should send an email with the seminar name, their name, and their Matrikelnummer to the Student Advising Office („Studienbüro“ https://www.soziologie.uni-mainz.de/studienbuero/). For technical reasons, it is unfortunately not possible to register independently.

Anwesenheitspflicht

Anyone who is absent in the first session without excuse risks losing their place. A prior informal excuse to the teacher by e-mail is sufficient. No medical certificate is required.

Recommended reading list

The following literature is expected to be read before the corresponding session (please find the links/pdf files uploaded in Moodle, and please note that some of the chapters are included in different sessions):

Monday 13th July:

Ahrweiler, P., Abe, J., Neumann M. (2025): Using a case study approach for investigating the status quo and future options of Al-based social assessment in public service provision. In: Ahrweiler, P. (Ed.), Participatory Artificial Intelligence in Public Social Services. From Bias to Fairness in Assessing Beneficiaries. Springer: Cham. https://doi.org/10.1007/978-3-031-71678-2_1
Fourcade, M. (2016). Ordinalization: Lewis A. Coser Memorial Award for Theoretical Agenda Setting 2014. Sociological Theory, 34(3), 175-195. https://doi.org/10.1177/0735275116665876

Tuesday 14th July:

Ahrweiler, P., Spaeth, E., Siqueiros Garcia, J.M., Luque Capellas, B., Wurster D. (2025): Inclusive technology co-design for participatory Al. In: Ahrweiler, P. (Ed.), Participatory Artificial Intelligence in Public Social Services. From Bias to Fairness in Assessing Beneficiaries. Springer: Cham. https://doi.org/10.1007/978-3-031-71678-2_2
Ahrweiler, P., Abe, J., Neumann M. (2025): Using a case study approach for investigating the status quo and future options of Al-based social assessment in public service provision. In: Ahrweiler, P. (Ed.), Participatory Artificial Intelligence in Public Social Services. From Bias to Fairness in Assessing Beneficiaries. Springer: Cham. https://doi.org/10.1007/978-3-031-71678-2_1
Sabater Coll, A., López, B., Campdepadrós, R., Sánchez, C. (2025). Participatory Action Research for AI in Social Services: An Example of Local Practices from Catalonia. In Ahrweiler, P. (Ed.), Participatory Artificial Intelligence in Public Social Services. From Bias to Fairness in Assessing Beneficiaries. Cham: Springer. https://doi.org/10.1007/978-3-031-71678-2_4
Sabater, A., López, B., Payarol, S., de Palau, I. (2026). Modelling Together for AI-Based Social Services. In: Ahrweiler, P., Gilbert, N. (Eds.), Participatory Modelling and Simulation to Improve AI-based Public Social Services. Scientific Advice for Policy and Practice. Cham : Springer. https://doi.org/10.1007/978-3-032-15283-1_3
Späth, E. (2025). AI Use in the Asylum Procedure in Germany: Exploring Perspectives with Refugees and Supporters on Assessment Criteria and Beyond. In: Ahrweiler, P. (Ed.), Participatory Artificial Intelligence in Public Social Services. From Bias to Fairness in Assessing Beneficiaries. Cham: Springer. https://doi.org/10.1007/978-3-031-71678-2_6
Späth, E., Bicket, M., Neumann, M., Wurster, D., Luque Capellas, B. (2026). Using Agent-Based Modelling to Explore Possible Implications of AI Use in the Asylum Procedure in Germany. In: Ahrweiler, P., Gilbert, N. (Eds.), Participatory Modelling and Simulation to Improve AI-based Public Social Services. Scientific Advice for Policy and Practice. Cham : Springer. https://doi.org/10.1007/978-3-032-15283-1_4

Wednesday 9th April:

Squazzoni, F., Jager, W., Edmonds, B. (2014). Social Simulation in the Social Sciences: A Brief Overview. Social Science Computer Review, 32(3), 279-294. https://doi.org/10.1177/0894439313512975
Ahrweiler, P., Gilbert, N., Bicket, M., Sabater Coll, A., Luque Capellas, B., Wurster, D., Siqueiros, J., Späth E. (2024): Gamification and Simulation for Innovation. In: Elsenbroich, C. and H. Verhagen (Eds.), Advances in Social Simulation. Springer Proceedings in Complexity. Springer, Cham: 121-136. https://doi.org/10.1007/978-3-031-57785-7_11
Sabater, A., López, B., Payarol, S., de Palau, I. (2026). Modelling Together for AI-Based Social Services. In: Ahrweiler, P., Gilbert, N. (Eds.), Participatory Modelling and Simulation to Improve AI-based Public Social Services. Scientific Advice for Policy and Practice. Cham : Springer. https://doi.org/10.1007/978-3-032-15283-1_3
Späth, E., Bicket, M., Neumann, M., Wurster, D., Luque Capellas, B. (2026). Using Agent-Based Modelling to Explore Possible Implications of AI Use in the Asylum Procedure in Germany. In: Ahrweiler, P., Gilbert, N. (Eds.), Participatory Modelling and Simulation to Improve AI-based Public Social Services. Scientific Advice for Policy and Practice. Cham: Springer. https://doi.org/10.1007/978-3-032-15283-1_4
Männiste, M., Vihalemm, T., Trumm, A. (2026). Gamifying Fairness: Exploring Algorithmic Decision-Making in Estonia’s Welfare System. In: Ahrweiler, P., Gilbert, N. (Eds.), Participatory Modelling and Simulation to Improve AI-based Public Social Services. Artificial Intelligence, Simulation and Society. Cham: Springer. https://doi.org/10.1007/978-3-032-15283-1_5
Luque Capellas, B. (2025). Algorithmic governance and social categorization in drought management: a working paper [working paper]. SSRN. Retrieved 17 June 2026, from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5587250
Cowls, J., Tsamados, A., Taddeo, M., Floridi, L. (2023): The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations. AI & Soc 38: 283–307. https://doi.org/10.1007/s00146-021-01294-x.
Galaz, V., Centeno, M. A., Callahan, P. W., Causevic, A., Patterson, T., Brass, I., Baum, S., Farber, D., Fischer, J., Garcia, D., McPhearson, T., Jimenez, D., King, B., Larcey, P., Levy, K. (2021). Artificial intelligence, systemic risks, and sustainability. Technology in Society, 67, 101741. https://doi.org/10.1016/j.techsoc.2021.101741

Overview:
The "TISSS Lab Summer School: Artificial Intelligence, Simulation and Society" explores the triad of „AI, Simulation, Society“ for two highly relevant and highly sensitive Innovation Areas: (1) AI use in assessing potential beneficiaries for public social services, and (2) AI use to mitigate climate crisis risks in natural disaster response. In both areas, the course deals with sociological aspects of AI futures and our ability to shape, test, and prototype potential techno-futures by sociological methods such as serious games in participatory social research and social simulation.

Innovation Area 1: AI use in assessing beneficiaries for public social service provision.
AI technologies are increasingly applied in assessing people as beneficiaries. However, the use of AI is challenged for its fairness: Existing biases and discrimination in service provision appear to be perpetuated as result of machine learning on past data. Fairness, however, is a cultural concept: its meaning in terms of values and beliefs, its implications for technology design, and the desired techno-futures need to be societally negotiated.

The School will start with a series of contents-related sessions with lectures on

introducing the challenge to provide participatory AI responsive to societal needs
reviewing existing systems
anticipating and projecting future systems

This will be followed by methods-related sessions where participants will learn how specific formats can help to bring sociological aspects to AI development. They learn, partly by lectures but also by direct experience, how

gamification, i.e., applying game elements in non-game contexts, can act as a low-threshold entry point for people to contribute to research
games can be designed to explore how people would create better systems from their perspective
the gamification approach can empower participants to deal with the problem of distributing scarce resources in the discussion and negotiation context of their specific socio-cultural setting
gamified solutions can work as input for simulations of the desired system leading to further discussions and deliberations
simulations can use agent-based modelling to reflect the gamified social context as a second-order construction of participants
they can observe the outcome of their design decisions in the simulation and use this for further iterations between game and simulation improving outcomes.

Innovation Area 2: AI use to mitigate climate crisis risks in natural disaster response.
AI already increases our resilience and our capacity dealing with a broad range of ecological crisis issues; there are many AI applications in natural disaster management with focus on flood, heat, fire, draught, etc., e.g. forecasting extreme weather events and disaster prediction, sensor networks and automated decision systems. However, full use of deep computation for smart solutions to keep up with accelerated crisis scenarios is not yet implemented. AI is accused of a gap between technology and society: For broad uptake and unfolding its expected transformation potential, AI would need to be more responsive to societal needs, more ethical, responsible and participatory.

In this part of the Spring School, participants will apply their learnings in small working groups and develop own contributions analogous to the workflow and methods of Innovation Area 1. Supported by the instructors, they will develop contents-related presentations

introducing the challenge to provide participatory AI responsive to societal needs
reviewing existing systems
anticipating and projecting future systems

followed by methods-related contributions such as serious games and ideas for simulation.

Learning outcomes:
By the end of the course participants will:

Understand the sociological aspects of AI use in complex social systems
Understand the concept of participation and its relation to inclusive technology co-design
Know and understand the substantive problems, theories and models in the two innovation areas (AI use for assessing beneficiaries for public social services; AI use to mitigate climate crisis risks in natural disaster response)
Understand mixed-methods approaches in the social sciences
Understand the use of serious games and simulation in sociology
Be able to conceptualise a serious game
Be able to apply a set of good practices for developing a simulation model
Be able to reflect upon the strength and pitfalls of gamification and social simulation

Assignments:
The assignments of this Summer School consist of three group-work contributions: (i) a presentation on a topic of Innovation Area 2 to be identified and discussed in the course; (ii) a serious game design to engage in co-design activities for Innovation Area 2; (iii) and an ABM flowchart sketching the idea for a simulation model in Innovation Area 2. All assignments will be developed during the sessions. Results will be presented and delivered on the last day of the Summer School.

TISSS Lab Summer School as part of "FORTHEM-Collective short-term mobility" program
This Summer School has been funded by the FORTHEM Alliance (https://www.forthem-alliance.eu) as a FORTHEM-Collective short-term mobility. Students from other FORTHEM universities will participate, and therefore the Summer School will involve international exchange.

Additional information

In modules 09 and 10 (core subject) and module 06 (minor subject) , one “Vertiefungsseminar“ and one “Wahlveranstaltung“ must be taken.

A term paper must be written in one of the seminars per module. The topic of the term paper must be discussed in advance with the supervisor. Assignments whose topic has not been discussed in advance may be rejected. This does not replace registration for the examination in Jogustine.

Further information (in German) on those courses (“Vertiefungsseminare” and “Wahlveranstaltungen“) can be found here: https://www.soziologie.uni-mainz.de/studium/studierende/#modul-vertiefungs-uns-wahlveranstaltungen

Further information on writing a term paper can be found here: https://www.soziologie.uni-mainz.de/downloads

The exact Summer School dates:
Monday: 14:00 - 18:00
Tuesday: 10:00 - 13:00 and 14:30 - 18:00
Wednesday: 10:00 - 13:00 and 14:30 - 17:00
Thursday: 9:00 - 12:15 and 14:30 - 18:00
Friday: 10:00 - 13:00

Dates