Why Should I Choose You? AutoXAI: A Framework for Selecting and Tuning
eXplainable AI Solutions

Why Should I Choose You? AutoXAI: A Framework for Selecting and Tuning eXplainable AI Solutions

6 October 2022

G. Roman-Jimenez

Papers citing "Why Should I Choose You? AutoXAI: A Framework for Selecting and Tuning eXplainable AI Solutions"

9 / 9 papers shown

Title
Beyond the Veil of Similarity: Quantifying Semantic Continuity in Explainable AI Qi Huang Emanuele Mezzi Osman Mutlu Miltiadis Kofinas Vidya Prasad Shadnan Azwad Khan Elena Ranguelova Niki van Stein 70 0 0 17 Jul 2024
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI Meike Nauta Jan Trienes Shreyasi Pathak Elisa Nguyen Michelle Peters Yasmin Schmitt Jorg Schlotterer M. V. Keulen C. Seifert ELM XAI 81 409 0 20 Jan 2022
Questioning the AI: Informing Design Practices for Explainable AI User Experiences Q. V. Liao D. Gruen Sarah Miller 115 715 0 08 Jan 2020
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 113 6,251 0 22 Oct 2019
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques Vijay Arya Rachel K. E. Bellamy Pin-Yu Chen Amit Dhurandhar Michael Hind ... Karthikeyan Shanmugam Moninder Singh Kush R. Varshney Dennis L. Wei Yunfeng Zhang XAI 59 392 0 06 Sep 2019
AutoML: A Survey of the State-of-the-Art Xin He Kaiyong Zhao Xiaowen Chu 109 1,453 0 02 Aug 2019
On the Robustness of Interpretability Methods David Alvarez-Melis Tommi Jaakkola 70 526 0 21 Jun 2018
The Mythos of Model Interpretability Zachary Chase Lipton FaML 160 3,685 0 10 Jun 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 969 16,931 0 16 Feb 2016