Citizen Council on AI

On 21st September, the Citizens' Assembly "Artificial Intelligence and Freedom" met for the first time at the University of Tübingen. 

This was the first of four meetings in which the randomly drawn participants will learn about artificial intelligence and form an opinion on how science and society can jointly shape future research. Based on this, they will write up and submit specific recommendations for the Ministry of Science, Research and the Arts Baden-Württemberg (MWK).

 As part of the initiative "KI macht Schule" I took part in this first installment which was mainly aimed at introducing the participants to each other and to the topic of artificial intelligence. In the afternoon the citizens had the opportunity to engage with and explore different uses of artificial intelligence in an interactive exhibit, which I put together with two colleagues from the university, Lisa Haxel and Kerstin Rau, as well as the ["KIMakerspace"]( https://ki-maker.space/).

As AI permeates our everyday lives to a degree that is hard to ignore, how we do research will ultimately also have a great impact on a lot of lives. Considering that most of our research is also financed, funded or subsidized with public money, getting citizens on board with AI research and giving them the opportunity to have a say in how it should be orientated, is therefore an important effort. 

I am really humbled that I was able to play a small part in this and I am immensely curious what the council will draft up. I really hope that the Citizens' Assembly "Artificial Intelligence and Freedom" will not be the last and hope that it has a signalling effect for the broader German / European research landscape.

For more details see the articles of the Reth AI, Cyber Valley and Citzen Assemblies Initiative

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Author

Jonas Beck is interested in probabilistic inference in mechanistic models of neuroscience. More specifically, he applies probabilistic numerical methods and simulation-based inference to fit Hodgkin-Huxley models to observed (experimental) data as part of the PIMMS Network Project of the Cluster of Excellence - Machine Learning for Science.

Department

Data Science