|TOCCHETTI ANDREA||Cycle: XXXV|
Section: Computer Science and Engineering
Advisor: BRAMBILLA MARCO
Tutor: MARTINENGHI DAVIDE
Major Research topic:
Model Explainability through Human Knowledge and Crowdsourcing
The spread of AI and black-box machine learning models, i.e., high accuracy models that sacrifice their understandability for their performances, made it necessary to explain their behaviour. Consequently, the research field of Explainable AI was born. The main objective of an Explainable AI system is to be understood by a human as the final beneficiary of the model. In our research, we frame the explainability problem from the crowd’s point of view, covering both well-known NLP and image tasks. We explore various crowd engagement methods. We develop methodologies to collect and organise human knowledge for explainability through gamification and games with a purpose (G.W.A.P.). We employ such structured knowledge to bridge the model and human gap, evaluating and improving different aspects of models’ explanations.
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