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"

17 / 17 papers shown

Title
Avoiding Leakage Poisoning: Concept Interventions Under Distribution Shifts M. Zarlenga Gabriele Dominici Pietro Barbiero Z. Shams M. Jamnik KELM 108 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 Viet Quoc Pham 29 8 0 30 May 2024
Understanding Inter-Concept Relationships in Concept-Based Models Naveen Raman M. Zarlenga M. Jamnik 22 4 0 28 May 2024
On the Relationship Between Interpretability and Explainability in Machine Learning Benjamin Leblanc Pascal Germain FaML 24 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 11 10 0 02 Oct 2023
SHARCS: Shared Concept Space for Explainable Multimodal Learning Gabriele Dominici Pietro Barbiero Lucie Charlotte Magister Pietro Lio' Nikola Simidjievski 15 4 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 56 38 0 27 Apr 2023
Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability Indro Spinelli Michele Guerra F. Bianchi Simone Scardapane 25 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-jin Yang 19 10 0 17 Oct 2022
Requirements Engineering for Machine Learning: A Review and Reflection Zhong Pei Lin Liu Chen Wang Jianmin Wang VLM 26 22 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 71 52 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' 14 21 0 27 Jul 2022
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges M. Bronstein Joan Bruna Taco S. Cohen Petar Velivcković GNN 172 1,100 0 27 Apr 2021
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI Alon Jacovi Ana Marasović Tim Miller Yoav Goldberg 244 422 0 15 Oct 2020
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems J. Willard X. Jia Shaoming Xu M. Steinbach Vipin Kumar AI4CE 83 387 0 10 Mar 2020
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 243 11,659 0 09 Mar 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 225 3,672 0 28 Feb 2017