Explainable and trustworthy AI (2025/2026)

Explainable and trustworthy AI (2025/2026)

General Information

SSD: ING-INF/05

CFU: 6

Lecturer: Eliana Pastor

Teaching Staff: Gabriele Ciravegna, Eleonora Poeta

⏰ Schedule

  • Thursday, 16:00-19:00 – classroom 14
  • Friday, 8:30-10:00 –  classroom 2I

📚 Teaching material

  • Course introduction (slides)
  • Trustworthy AI: definition and motivations (slides)
  • Explainable AI: taxonomy (slides)
  • Pre-modeling explainability (slides)
  • In-modeling explainability (slides)
  • Post-hoc model agnostic – global (slides)
  • Post-hoc model agnostic – local surrogate models (slides, Q&A interactive session)
  • Post-hoc model agnostic – local – explaining by removing/perturbing (slides

💻 Laboratory Material

  • Lab 0 – ML Pipeline Review: Scikit-learn & PyTorch (zip)
  • Lab 1 – Interpretable by Design Models (zip) (solution) (pdf_solution)
  • Lab 2 – Post-hoc global explanation methods (zip) (solution) (pdf_solution)
  • Lab 3a –  Post-hoc local explanation methods – LIME (zip)