XAI and philosophical work on explanation: A roadmap

Aleks Knoks, Thomas Raleigh

Research output: Contribution to journalConference articlepeer-review


What Deep Neural Networks (DNNs) can do is impressive, yet they are notoriously opaque. Responding to the worries associated with this opacity, the field of XAI has produced a plethora of methods purporting to explain the workings of DNNs. Unsurprisingly, a whole host of questions revolves around the notion of explanation central to this field. This note provides a roadmap of the recent work that tackles these questions from the perspective of philosophical ideas on explanations and models in science.

Original languageEnglish
Pages (from-to)101-106
Number of pages6
JournalCEUR Workshop Proceedings
Publication statusPublished - 2022
Externally publishedYes
Event1st Workshop on Bias, Ethical AI, Explainability and the Role of Logic and Logic Programming, BEWARE 2022 - Udine, Italy
Duration: Dec 2 2022 → …


  • Black Box Problem
  • Deep Neural Networks
  • Explainable Artificial Intelligence
  • explanation
  • scientific models
  • understanding

ASJC Scopus subject areas

  • General Computer Science


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