Trust in AI: Interpretability is not necessary or sufficient, while black-box interaction is necessary and sufficient

10 February 2022

Papers citing "Trust in AI: Interpretability is not necessary or sufficient, while black-box interaction is necessary and sufficient"

13 / 13 papers shown

Title
Avoiding Leakage Poisoning: Concept Interventions Under Distribution Shifts M. Zarlenga Gabriele Dominici Pietro Barbiero Z. Shams M. Jamnik KELM 488 0 0 24 Apr 2025
Applications of Generative AI (GAI) for Mobile and Wireless Networking: A Survey Thai-Hoc Vu Senthil Kumar Jagatheesaperumal Minh-Duong Nguyen Nguyen Van Huynh Sunghwan Kim Quoc-Viet Pham 94 13 0 30 May 2024
Understanding Inter-Concept Relationships in Concept-Based Models Naveen Raman M. Zarlenga M. Jamnik 95 5 0 28 May 2024
On the Relationship Between Interpretability and Explainability in Machine Learning Benjamin Leblanc Pascal Germain FaML 120 0 0 20 Nov 2023
A Framework for Interpretability in Machine Learning for Medical Imaging Alan Q. Wang Batuhan K. Karaman Heejong Kim Jacob Rosenthal Rachit Saluja Sean I. Young M. Sabuncu AI4CE 134 13 0 02 Oct 2023
SHARCS: Shared Concept Space for Explainable Multimodal Learning Gabriele Dominici Pietro Barbiero Lucie Charlotte Magister Pietro Lio Nikola Simidjievski 82 6 0 01 Jul 2023
Interpretable Neural-Symbolic Concept Reasoning Pietro Barbiero Gabriele Ciravegna Francesco Giannini M. Zarlenga Lucie Charlotte Magister Alberto Tonda Pietro Lio F. Precioso M. Jamnik G. Marra NAI LRM 145 41 0 27 Apr 2023
Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability Indro Spinelli Michele Guerra F. Bianchi Simone Scardapane 76 0 0 14 Apr 2023
A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities Andrea Tocchetti Lorenzo Corti Agathe Balayn Mireia Yurrita Philip Lippmann Marco Brambilla Jie Yang 87 14 0 17 Oct 2022
Requirements Engineering for Machine Learning: A Review and Reflection Zhong Pei Lin Liu Chen Wang Jianmin Wang VLM 78 24 0 03 Oct 2022
Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off M. Zarlenga Pietro Barbiero Gabriele Ciravegna G. Marra Francesco Giannini ... F. Precioso S. Melacci Adrian Weller Pietro Lio M. Jamnik 146 59 0 19 Sep 2022
Encoding Concepts in Graph Neural Networks Lucie Charlotte Magister Pietro Barbiero Dmitry Kazhdan F. Siciliano Gabriele Ciravegna Fabrizio Silvestri M. Jamnik Pietro Lio 84 21 0 27 Jul 2022
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.3K 17,225 0 16 Feb 2016