Machine Learning Explainability for External Stakeholders

10 July 2020

Papers citing "Machine Learning Explainability for External Stakeholders"

6 / 6 papers shown

Title
Robust Explanation Constraints for Neural Networks Matthew Wicker Juyeon Heo Luca Costabello Adrian Weller FAtt 19 17 0 16 Dec 2022
Perspectives on Incorporating Expert Feedback into Model Updates Valerie Chen Umang Bhatt Hoda Heidari Adrian Weller Ameet Talwalkar 30 11 0 13 May 2022
What will it take to generate fairness-preserving explanations? Jessica Dai Sohini Upadhyay Stephen H. Bach Himabindu Lakkaraju FAtt FaML 11 14 0 24 Jun 2021
A Multistakeholder Approach Towards Evaluating AI Transparency Mechanisms Ana Lucic Madhulika Srikumar Umang Bhatt Alice Xiang Ankur Taly Q. V. Liao Maarten de Rijke 20 5 0 27 Mar 2021
Pitfalls in Machine Learning Research: Reexamining the Development Cycle Stella Biderman Walter J. Scheirer 13 26 0 04 Nov 2020
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 225 3,681 0 28 Feb 2017