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Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability

12 September 2018

Papers citing "Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability"

21 / 21 papers shown

Title
Operationalizing the Blueprint for an AI Bill of Rights: Recommendations for Practitioners, Researchers, and Policy Makers Alex Oesterling Usha Bhalla Suresh Venkatasubramanian Himabindu Lakkaraju 76 3 0 11 Jul 2024
On the Power of Randomization in Fair Classification and Representation Sushant Agarwal Amit Deshpande FaML 83 5 0 05 Jun 2024
SoK: Taming the Triangle -- On the Interplays between Fairness, Interpretability and Privacy in Machine Learning Julien Ferry Ulrich Aïvodji Sébastien Gambs Marie-José Huguet Mohamed Siala FaML 69 5 0 22 Dec 2023
A Critical Survey on Fairness Benefits of Explainable AI Luca Deck Jakob Schoeffer Maria De-Arteaga Niklas Kühl 117 13 0 15 Oct 2023
LUCID-GAN: Conditional Generative Models to Locate Unfairness Andres Algaba Carmen Mazijn Carina E. A. Prunkl J. Danckaert Vincent Ginis SyDa 97 1 0 28 Jul 2023
Algorithms, Incentives, and Democracy E. M. Penn John W. Patty FaML 62 1 0 05 Jul 2023
Decongestion by Representation: Learning to Improve Economic Welfare in Marketplaces Omer Nahum Gali Noti David C. Parkes Nir Rosenfeld 73 3 0 18 Jun 2023
Cross-Institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and Equity Joshua Gardner Renzhe Yu Quan Nguyen Christopher A. Brooks René F. Kizilcec FedML 89 16 0 01 May 2023
LUCID: Exposing Algorithmic Bias through Inverse Design Carmen Mazijn Carina E. A. Prunkl Andres Algaba J. Danckaert Vincent Ginis SyDa 88 4 0 26 Aug 2022
Learning Optimal Predictive Checklists Haoran Zhang Q. Morris Berk Ustun Marzyeh Ghassemi 120 11 0 02 Dec 2021
On the Fairness of Machine-Assisted Human Decisions Talia B. Gillis Bryce Mclaughlin Jann Spiess FaML 64 16 0 28 Oct 2021
Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud Michaela Hardt Xiaoguang Chen Xiaoyi Cheng Michele Donini J. Gelman ... Muhammad Bilal Zafar Sanjiv Ranjan Das Kevin Haas Tyler Hill K. Kenthapadi ELM FaML 80 43 0 07 Sep 2021
MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset Chuizheng Meng Loc Trinh Nan Xu Yan Liu 67 30 0 12 Feb 2021
Removing Spurious Features can Hurt Accuracy and Affect Groups Disproportionately Fereshte Khani Percy Liang FaML 61 66 0 07 Dec 2020
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI Alon Jacovi Ana Marasović Tim Miller Yoav Goldberg 328 450 0 15 Oct 2020
A Possibility in Algorithmic Fairness: Can Calibration and Equal Error Rates Be Reconciled? Claire Lazar Reich Suhas Vijaykumar FaML 67 19 0 18 Feb 2020
$A Precise High-Dimensional Asymptotic Theory for Boosting and Minimum-$\ell_1$-Norm Interpolated Classifiers$ A Precise High-Dimensional Asymptotic Theory for Boosting and Minimum- $\ell_1$ -Norm Interpolated Classifiers Tengyuan Liang Pragya Sur 133 70 0 05 Feb 2020
Measurement and Fairness Abigail Z. Jacobs Hanna M. Wallach 90 404 0 11 Dec 2019
Fairness Sample Complexity and the Case for Human Intervention Ananth Balashankar Alyssa Lees 23 3 0 24 Oct 2019
SensitiveNets: Learning Agnostic Representations with Application to Face Images Aythami Morales Julian Fierrez R. Vera-Rodríguez Ruben Tolosana CVBM 97 42 0 01 Feb 2019
Manipulating and Measuring Model Interpretability Forough Poursabzi-Sangdeh D. Goldstein Jake M. Hofman Jennifer Wortman Vaughan Hanna M. Wallach 133 702 0 21 Feb 2018