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
Abstract:
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.
Cookies
We serve cookies. If you think that's ok, just click "Accept all". You can also choose what kind of cookies you want by clicking "Settings".
Read our cookie policy
Cookies
Choose what kind of cookies to accept. Your choice will be saved for one year.
Read our cookie policy
-
Necessary
These cookies are not optional. They are needed for the website to function. -
Statistics
In order for us to improve the website's functionality and structure, based on how the website is used. -
Experience
In order for our website to perform as well as possible during your visit. If you refuse these cookies, some functionality will disappear from the website. -
Marketing
By sharing your interests and behavior as you visit our site, you increase the chance of seeing personalized content and offers.