Iterative Orthogonal Feature Projection for Diagnosing Bias in Black-Box Models

15 November 2016

Papers citing "Iterative Orthogonal Feature Projection for Diagnosing Bias in Black-Box Models"

31 / 31 papers shown

Title
Discrimination-free Insurance Pricing with Privatized Sensitive Attributes Tianhe Zhang Suhan Liu Peng Shi FaML 74 0 0 16 Apr 2025
Parametric Fairness with Statistical Guarantees François Hu Philipp Ratz Arthur Charpentier FaML 15 1 0 31 Oct 2023
Fairness in Multi-Task Learning via Wasserstein Barycenters Franccois Hu Philipp Ratz Arthur Charpentier 39 10 0 16 Jun 2023
Adaptive Fairness Improvement Based on Causality Analysis Mengdi Zhang Jun Sun 24 31 0 15 Sep 2022
TESTSGD: Interpretable Testing of Neural Networks Against Subtle Group Discrimination Mengdi Zhang Jun Sun Jingyi Wang Bing-Jie Sun 21 14 0 24 Aug 2022
NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification Haibin Zheng Zhiqing Chen Tianyu Du Xuhong Zhang Yao Cheng S. Ji Jingyi Wang Yue Yu Jinyin Chen 27 51 0 25 Dec 2021
Algorithm Fairness in AI for Medicine and Healthcare Richard J. Chen Tiffany Y. Chen Jana Lipkova Judy J. Wang Drew F. K. Williamson Ming Y. Lu S. Sahai Faisal Mahmood FaML 73 45 0 01 Oct 2021
Fairness guarantee in multi-class classification Christophe Denis Romuald Elie Mohamed Hebiri Franccois Hu FaML 45 48 0 28 Sep 2021
Meaningfully Debugging Model Mistakes using Conceptual Counterfactual Explanations Abubakar Abid Mert Yuksekgonul James Zou CML 34 64 0 24 Jun 2021
Fair Preprocessing: Towards Understanding Compositional Fairness of Data Transformers in Machine Learning Pipeline Sumon Biswas Hridesh Rajan 26 112 0 02 Jun 2021
Transforming Feature Space to Interpret Machine Learning Models A. Brenning FAtt 50 9 0 09 Apr 2021
MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset Chuizheng Meng Loc Trinh Nan Xu Yan Liu 24 30 0 12 Feb 2021
Fairness in Machine Learning L. Oneto Silvia Chiappa FaML 256 492 0 31 Dec 2020
Fairkit, Fairkit, on the Wall, Who's the Fairest of Them All? Supporting Data Scientists in Training Fair Models Brittany Johnson Jesse Bartola Rico Angell Katherine Keith Sam Witty S. Giguere Yuriy Brun FaML 33 18 0 17 Dec 2020
Principles and Practice of Explainable Machine Learning Vaishak Belle I. Papantonis FaML 24 437 0 18 Sep 2020
Explainable Predictive Process Monitoring Musabir Musabayli F. Maggi Williams Rizzi Josep Carmona Chiara Di Francescomarino 19 60 0 04 Aug 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 49 371 0 30 Apr 2020
Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness Zhu Li Adrián Pérez-Suay Gustau Camps-Valls Dino Sejdinovic FaML 10 21 0 11 Nov 2019
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 41 6,125 0 22 Oct 2019
Measuring Unfairness through Game-Theoretic Interpretability Juliana Cesaro Fabio Gagliardi Cozman FAtt 16 13 0 12 Oct 2019
Metamorphic Testing of a Deep Learning based Forecaster Anurag Dwarakanath Manish Ahuja Sanjay Podder Silja Vinu Arijit Naskar M. Koushik AI4TS 16 9 0 13 Jul 2019
Disentangling Influence: Using Disentangled Representations to Audit Model Predictions Charles Marx R. L. Phillips Sorelle A. Friedler C. Scheidegger Suresh Venkatasubramanian TDI CML MLAU 13 27 0 20 Jun 2019
Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification Evgenii Chzhen Christophe Denis Mohamed Hebiri L. Oneto Massimiliano Pontil FaML 19 85 0 12 Jun 2019
General Fair Empirical Risk Minimization L. Oneto Michele Donini Massimiliano Pontil FaML 24 38 0 29 Jan 2019
Taking Advantage of Multitask Learning for Fair Classification L. Oneto Michele Donini Amon Elders Massimiliano Pontil FaML 17 60 0 19 Oct 2018
Automated Test Generation to Detect Individual Discrimination in AI Models Aniya Aggarwal P. Lohia Seema Nagar Kuntal Dey Diptikalyan Saha 15 40 0 10 Sep 2018
Empirical Risk Minimization under Fairness Constraints Michele Donini L. Oneto Shai Ben-David John Shawe-Taylor Massimiliano Pontil FaML 24 439 0 23 Feb 2018
A Survey Of Methods For Explaining Black Box Models Riccardo Guidotti A. Monreale Salvatore Ruggieri Franco Turini D. Pedreschi F. Giannotti XAI 43 3,904 0 06 Feb 2018
On the Direction of Discrimination: An Information-Theoretic Analysis of Disparate Impact in Machine Learning Hao Wang Berk Ustun Flavio du Pin Calmon FaML 9 11 0 16 Jan 2018
Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation S. Tan R. Caruana Giles Hooker Yin Lou MLAU 28 180 0 17 Oct 2017
Fairness Testing: Testing Software for Discrimination Sainyam Galhotra Yuriy Brun A. Meliou 13 376 0 11 Sep 2017