The workshop "Representative Signatures in Data Analysis - Epistemological Reflections" will bring together philosophers of science and colleagues from astroparticle physics, computer science, and statistics. It will focus on the way in which machine learning models manage to extract significant data from data sets that contain a huge background of irrelevant data. Current methods of statistical data analysis base the extraction of the significant data on probabilistic quality standards for representative signatures and employs machine learning models to extract the relevant data subsamples.
At the workshop, we want to address the following questions:
- How have the current machine learning methods transformed the analysis of observational data, compared to the scanning of data samples by human beings, for example in particle physics from the 1960s to the 1980s?
- To which kind of representation do the probabilistic quality standards implemented in the AI models give rise?
- In which sense and to what extent result these methods in representing real data or true events?
- Which role plays the "inverse problem" of tracing the measured raw data back to event distributions considered to be approximately true, from a philosophical point of view?
- To what extent is the representation of data by AI models compatible with scientific realism about the results of the data processing?
The workshop will take place on October 15-16 at TU Dortmund University.