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Archivio digitale delle tesi discusse presso l’Università di Pisa

Tesi etd-09122023-122950


Tipo di tesi
Tesi di laurea magistrale
Autore
RUSSO, FABIO MICHELE
URN
etd-09122023-122950
Titolo
Explainability for black box decisions on image data
Dipartimento
INFORMATICA
Corso di studi
DATA SCIENCE AND BUSINESS INFORMATICS
Relatori
relatore Prof.ssa Monreale, Anna
relatore Dott. Metta, Carlo
controrelatore Prof.ssa Sirbu, Alina
Parole chiave
  • ai
  • artificial intelligence
  • classification
  • explainability
  • machine learning
  • trustworthiness
Data inizio appello
06/10/2023
Consultabilità
Non consultabile
Data di rilascio
06/10/2093
Riassunto
Explainability for black box decisions on image data is a research thesis on explainability in image classification decisions. For the methodology, I build on a latent-space exemplars and counter exemplars based model-agnostic explainability algorithm, ABELE (Guidotti, Monreale, Matwin, Pedreschi, 2019). My contribution consists in the pipeline “oab” that consists in three steps: first, the training data is sampled for a subset that is offline explained with the original method, that forms the “explanation base”. Then, in the model's latent space, incoming test records are matched with the existing explanation base; finally the explanation base's rules and counterrules are used to explain the test record.
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