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Explainability
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Explainability
Understanding Stakeholders' Perceptions and Needs Across the LLM Supply Chain
Explainability and transparency of AI systems are undeniably important, leading to several research studies and tools addressing them. …
Agathe Balayn
,
Lorenzo Corti
,
Fanny Rancourt
,
Fabio Casati
,
Ujwal Gadiraju
Conference on Human Factors in Computing Systems (ACM-CHI), 2024.
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Vidéo
Explaining Graph Neural Networks Using Interpretable Local Surrogates
We propose an interpretable local surrogate (ILS) method for understanding the predictions of black-box graph models. Explainability …
Perouz Taslakian
,
Guillaume Rabusseau
,
Farzaneh Heidari
Workshop at the International Conference on Machine Learning (ICML), 2023.
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Citation
Explainable, Sensible and Virtuous Workplace Chatbots
We outline three research directions towards the practical implementation of explainable, sensible and virtuous chatbots for the …
Gabriel Huang
,
Valérie Bécaert
,
David Vazquez
Montreal AI Symposium (MAIS), 2022.
Citation
Explaining by Example: A Practitioner’s Perspective
Black-box machine learning (ML) models have become increasingly popular in practice. They can offer great performance, especially in …
Marc-Etienne Brunet
,
Masoud Hashemi
Montreal AI Symposium (MAIS), 2022.
Citation
Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations
Explainability for machine learning models has gained considerable attention within the research community given the importance of …
Pau Rodriguez
,
Massimo Caccia
,
Alexandre Lacoste
,
Lee Zamparo
,
Issam H. Laradji
,
Laurent Charlin
,
David Vazquez
International Conference on Computer Vision (ICCV), 2021.
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Citation
Code
RelatIF: Identifying Explanatory Training Examples via Relative Influence
In this work, we focus on the use of influence functions to identify relevant training examples that one might hope …
Elnaz Barshan
,
Marc-Etienne Brunet
,
Gintare Karolina Dziugaite
International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.
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