Switch to: References

Add citations

You must login to add citations.
  1. Towards a Benchmark for Scientific Understanding in Humans and Machines.Kristian Gonzalez Barman, Sascha Caron, Tom Claassen & Henk de Regt - 2024 - Minds and Machines 34 (1):1-16.
    Scientific understanding is a fundamental goal of science. However, there is currently no good way to measure the scientific understanding of agents, whether these be humans or Artificial Intelligence systems. Without a clear benchmark, it is challenging to evaluate and compare different levels of scientific understanding. In this paper, we propose a framework to create a benchmark for scientific understanding, utilizing tools from philosophy of science. We adopt a behavioral conception of understanding, according to which genuine understanding should be recognized (...)
    Download  
     
    Export citation  
     
    Bookmark   2 citations  
  • Reliability and Interpretability in Science and Deep Learning.Luigi Scorzato - 2024 - Minds and Machines 34 (3):1-31.
    In recent years, the question of the reliability of Machine Learning (ML) methods has acquired significant importance, and the analysis of the associated uncertainties has motivated a growing amount of research. However, most of these studies have applied standard error analysis to ML models—and in particular Deep Neural Network (DNN) models—which represent a rather significant departure from standard scientific modelling. It is therefore necessary to integrate the standard error analysis with a deeper epistemological analysis of the possible differences between DNN (...)
    Download  
     
    Export citation  
     
    Bookmark