Interpretable Network Visualizations: A Human-in-the-Loop Approach for Post-hoc Explainability of CNN-based Image Classification

1 code implementation • 6 May 2024 • Matteo Bianchi, Antonio De Santis, Andrea Tocchetti, Marco Brambilla

Transparency and explainability in image classification are essential for establishing trust in machine learning models and detecting biases and errors.

Image Classification Sentence

A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities

1 code implementation • 17 Oct 2022 • Andrea Tocchetti, Lorenzo Corti, Agathe Balayn, Mireia Yurrita, Philip Lippmann, Marco Brambilla, Jie Yang

Despite the impressive performance of Artificial Intelligence (AI) systems, their robustness remains elusive and constitutes a key issue that impedes large-scale adoption.