CALL FOR
PARTNER INSTITUTIONS

for an international workshop series on AI technologies on five continents in 2024

Exploring and Enhancing Nuanced and Informed Public Consideration of AI Technologies: A Cross-national Workshop Series as Spaces for Discourse

A research project by researchers from

Supported by

We are currently looking for partner institutions on five different continents (South America, Africa, Asia, Europe, and North America). Please take a look at further information below by clicking on respective sections.

Please feel free to reach out to Michel Hohendanner (he/him) in case of interest or potential questions via michel.hohendanner[at]hm.edu

WHAT & FOR WHOM

The international workshop series explores cross-national local perspectives on emerging AI technologies by providing citizens with learning spaces and enabling them to reflect on the influence of AI technology and to design discussion contributions.

HOW?

We combine design futuring methods with methods from technology assessment to engage citizen in reflective and discursive processes about socio-technical systems.

WHERE?

Five countries, one from each: South America, Africa, Asia, Europe, and North America (countries to be defined based on partner institutions; important: voices from global South).

WHY?

Oftentimes technologies are developed in a specific local context but imposed on citizens globally. When these technologies emerge “suddenly” citizens have no possibility to obtain a solid basic/general knowledge. With our workshop series we aim to increase their agency and broaden discourses.

WE OFFER

  • A robust workshop process (adaptable to local contexts together with local partners) for citizen participation and public learning
  • Train-the-trainer workshops for facilitators
  • Covering local expenses, e.g., location, snacks and drinks, material costs, translation of workshop material
  • The possibility for you to become a contributor to an international endeavor of exploring citizens’ perceptions and building a workshop tool to assess the public’s opinions on genAI and FPT
  • Possibility to build partnerships with the two universities TUM and HM for future cooperations
  • Frameworks and guidelines to enable institutions to recreate workshops independently

PARTNERS SHOULD PROVIDE

  • Possibilities to reach out to local citizens and to recruit a diverse group of participants (communication infrastructure)
  • Possibilities to conduct a workshop with 50+ participants (location/physical infrastructure)
  • Translation of workshop material
  • Capabilities to participate in the analysis of emerging results

In the course of the research project, we would like to explore citizens‘ perceptions of AI systems. The primary aim is to discover which concrete (sometimes future) use cases could be considered desirable or explicitly undesirable by the participants. This will be carried out with a focus on the local-specific contexts and needs of the participants. In order to make the broad field of AI applications tangible and to be able to illuminate different positions, we distinguish between two types of systems:

„Subordinative“ AI systems, e.g., facial processing technologies (FPT)

Subordinative AI systems are imposed on citizens, who have no possibility to influence the system’s functions, or sometimes even be aware of the systems being applied.

„Integrative“ AI systems, e.g., generative AI systems like intelligent chat systems or text-to-image generators

Integrative AI systems allow citizens to co-create content with the system and to adapt or adopt it to their individual contexts.

GOALS

  1. Train citizens in socio-technical discursive practice to gain agency within socio-technical systems: citizens are enabled to develop informed opinions, critically reflect on ethical and societal concerns, and explore future implications and (un-) desirable applications for their personal purposes of both systems.

  2. Inform HCI and AI-development communities about cross-cultural perceptions on AI (FPT, text-to-image, intelligent chat systems).

MEASURES

  • Participant’s reflection capabilities (pre- and post-test)
  • Social media engagement w/ posted content (likes, shares, views)
  • Presentation/publication at 3 international festivals/ conferences
  • Mapping of locally-specific factors for adoption & development
  • Long-term impact: workshop & exhibition guide (download, print)

This research project is hosted by the Chair of Cyber Trust at the Technical University of Munich (TUM) with Prof. Jens Grossklags and carried out by researchers from the Chair of Cyber Trust at TUM and Munich Center for Digital Sciences and AI (MUCDAI) at the University of Applied Sciences Munich.

Prof. Grossklags holds the Associate Professorship for Cyber Trust and directs the Chair of Cyber Trust at the Department of Computer Science in the TUM School of Computation, Information and Technology at the Technical University of Munich, since January 2017. His published research has appeared in high quality publications at technical and social science venues, and has been honored with best paper awards. A list of publications is available on this site.

Chiara Ullstein is a Research Associate as well as Doctoral Candidate at the Chair of Cyber Trust, TUM. With a background in Politics and Technology, Chiara’s research explores public participation in the development and regulation of AI applications. Chiara applies both qualitative and quantitative research methods. Her recent work focuses on public perception of facial processing technologies.

Michel Hohendanner is Research Associate at the Munich Center for Digital Sciences and AI (MUCDAI) and Doctoral Candidate at the Chair of Cyber Trust (TUM). His research focuses on the social impact of new emerging technologies like (generative) AI and social aspects of immersive technologies. His investigations are driven and informed by (speculative) design as method of inquiry, digital ethics, and human-computer interaction.

Selection of previous work on public perception and participation in socio-technical contexts: