Munich Center for Mathematical Philosophy (MCMP)
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Philosophy of Artificial Intelligence

Our research in the philosophy of Artificial Intelligence (AI) addresses general questions concerning the methodology and foundations of AI and the role of AI in the sciences, philosophy, society, and industry. Such general questions are concerned with the following issues:

  • Epistemology of Machine Learning: In what sense do machine learning algorithms learn? What is the rational way to learn from evidence? How can we get from examples to hypotheses? Does machine learning solve the problem of induction? How should we represent causal reasoning and reasoning under uncertainty formally? Is deep learning epistemically well-grounded? Does inference in artificial neural networks conform to a logical system?
  • Explainable AI: What defines good explanations? Is there a difference between explanations, interpretations, and justifications? In which situations do we demand explanations for AI decisions? Are all (of these) explanations causal? In what sense are some algorithms black-boxes and others not? Is there a trade-off between explainability and accuracy? Which methods are there to make AI more transparent? How can we bridge traditional logical AI and contemporary machine learning?
  • Artificial Agency and Ethics of AI: What do we mean by autonomous agents? Must autonomous AI have consciousness? Is there a way of defining consciousness in formal terms? What defines an intelligent agent, action or reasoning? Must an intelligent agent also be a moral agent? Can we implement ethical rules into AI systems? Should we rely on algorithms in safety-critical domains? What are the biases of machine learning algorithms? How can we make machine learning algorithms fair and secure?
  • AI, Philosophy, and Science: How do AI and philosophy of science relate to each other? What can they learn from each other? Which philosophical challenges does modern AI research face? Is machine learning a buzzword for statistics/cognitive science? If not, what does it add? What role does machine learning play in other sciences? Will machine learning substitute statistics in the near future? Or science as we know it? Can we automatize causal discovery?

Members of faculty working on philosophy of artificial intelligence:

Doctoral fellows working in philosophy of artificial intelligence: