Multi-criteria Rank-based Aggregation for Explainable AI

30 May 2025

Sujoy Chatterjee

Everton Romanzini Colombo

Marcos Medeiros Raimundo

XAI

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Papers citing "Multi-criteria Rank-based Aggregation for Explainable AI"

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Title
Provably Better Explanations with Optimized Aggregation of Feature Attributions Thomas Decker Ananta R. Bhattarai Jindong Gu Volker Tresp Florian Buettner 56 3 0 07 Jun 2024
T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients Evandro S. Ortigossa Fábio F. Dias Brian Barr Claudio T. Silva L. G. Nonato FAtt 84 3 0 25 Apr 2024
A comprehensive study on fidelity metrics for XAI Miquel Miró-Nicolau Antoni Jaume-i-Capó Gabriel Moyà Alcover 46 11 0 19 Jan 2024
Accelerating the Global Aggregation of Local Explanations Alon Mor Yonatan Belinkov B. Kimelfeld FAtt 39 4 0 13 Dec 2023
Axiomatic Aggregations of Abductive Explanations Gagan Biradar Yacine Izza Elita Lobo Vignesh Viswanathan Yair Zick FAtt 56 6 0 29 Sep 2023
Discriminative Feature Attributions: Bridging Post Hoc Explainability and Inherent Interpretability Usha Bhalla Suraj Srinivas Himabindu Lakkaraju FAtt CML 60 7 0 27 Jul 2023
Explainability in Machine Learning: a Pedagogical Perspective Andreas Bueff I. Papantonis Auste Simkute Vaishak Belle 36 2 0 21 Feb 2022
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective Satyapriya Krishna Tessa Han Alex Gu Steven Wu S. Jabbari Himabindu Lakkaraju 228 190 0 03 Feb 2022
GLocalX -- From Local to Global Explanations of Black Box AI Models Mattia Setzu Riccardo Guidotti A. Monreale Franco Turini D. Pedreschi F. Giannotti 39 119 0 19 Jan 2021
Interpretability and Explainability: A Machine Learning Zoo Mini-tour Ricards Marcinkevics Julia E. Vogt XAI 63 120 0 03 Dec 2020
Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring Michael Bücker G. Szepannek Alicja Gosiewska P. Biecek FaML 33 112 0 28 Sep 2020
Evaluating and Aggregating Feature-based Model Explanations Umang Bhatt Adrian Weller J. M. F. Moura XAI 75 219 0 01 May 2020
ViCE: Visual Counterfactual Explanations for Machine Learning Models Oscar Gomez Steffen Holter Jun Yuan E. Bertini AAML 75 96 0 05 Mar 2020
Can I Trust the Explainer? Verifying Post-hoc Explanatory Methods Oana-Maria Camburu Eleonora Giunchiglia Jakob N. Foerster Thomas Lukasiewicz Phil Blunsom FAtt AAML 39 61 0 04 Oct 2019
Global Aggregations of Local Explanations for Black Box models I. V. D. Linden H. Haned Evangelos Kanoulas FAtt 33 65 0 05 Jul 2019
Aggregating explanation methods for stable and robust explainability Laura Rieger Lars Kai Hansen AAML FAtt 51 12 0 01 Mar 2019
Towards Robust Interpretability with Self-Explaining Neural Networks David Alvarez-Melis Tommi Jaakkola MILM XAI 105 938 0 20 Jun 2018
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 552 21,613 0 22 May 2017
European Union regulations on algorithmic decision-making and a "right to explanation" B. Goodman Seth Flaxman FaML AILaw 58 1,888 0 28 Jun 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 587 16,828 0 16 Feb 2016