Katya Tentori — accepted AI-conference papers

2026

Human Cognitive Biases in Explanation-based Interaction: The Case of Within and Between Session Order Effect

Dario Pesenti, Alessandro Bogani, Katya Tentori, Stefano Teso

AAAI 2026technical

Explanatory Interactive Learning (XIL) is a powerful interactive learning framework designed to enable users to customize and correct AI models by interacting with their explanations. In a nutshell, XIL algorithms select a number of items on which an AI model made a decision (e.g., images and their

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2023

Concept-level Debugging of Part-Prototype Networks

Andrea Bontempelli, Stefano Teso, Katya Tentori, Fausto Giunchiglia, Andrea Passerini

ICLR 2023top-25%

Part-prototype Networks (ProtoPNets) are concept-based classifiers designed to achieve the same performance as black-box models without compromising transparency. ProtoPNets compute predictions based on similarity to class-specific part-prototypes learned to recognize parts of training examples, mak…

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