JGU, 7-11 April 2025, SoSe 2025
Instructors: Prof. Dr. Petra Ahrweiler; Blanca Luque Capellas M.A., Dr. Martin Neumann
Language of instruction: English
Overview:
The Spring School 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.
See full description here.
This course is open to exchange students (of all subjects). To register, please contact the Sociology Office (studienbuero.soziologie@uni-mainz.de). Up to 30 students will be accepted.
