Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation

17 October 2017

Papers citing "Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation"

31 / 31 papers shown

Title
Explaining Probabilistic Models with Distributional Values Luca Franceschi Michele Donini Cédric Archambeau Matthias Seeger FAtt 39 2 0 15 Feb 2024
Active Globally Explainable Learning for Medical Images via Class Association Embedding and Cyclic Adversarial Generation Ruitao Xie Jingbang Chen Limai Jiang Ru Xiao Yi-Lun Pan Yunpeng Cai GAN MedIm 27 0 0 12 Jun 2023
Interpretable Differencing of Machine Learning Models Swagatam Haldar Diptikalyan Saha Dennis L. Wei Rahul Nair Elizabeth M. Daly 16 1 0 10 Jun 2023
Explaining black box text modules in natural language with language models Chandan Singh Aliyah R. Hsu Richard Antonello Shailee Jain Alexander G. Huth Bin-Xia Yu Jianfeng Gao MILM 36 47 0 17 May 2023
How to address monotonicity for model risk management? Dangxing Chen Weicheng Ye 21 5 0 28 Apr 2023
Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties Nikolaos N. Vlassis WaiChing Sun AI4CE DiffM 22 46 0 24 Feb 2023
Monotonicity for AI ethics and society: An empirical study of the monotonic neural additive model in criminology, education, health care, and finance Dangxing Chen Luyao Zhang SyDa 35 6 0 17 Jan 2023
Testing the effectiveness of saliency-based explainability in NLP using randomized survey-based experiments Adel Rahimi Shaurya Jain FAtt 18 0 0 25 Nov 2022
Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models Esma Balkir S. Kiritchenko I. Nejadgholi Kathleen C. Fraser 21 36 0 08 Jun 2022
The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations Aparna Balagopalan Haoran Zhang Kimia Hamidieh Thomas Hartvigsen Frank Rudzicz Marzyeh Ghassemi 43 78 0 06 May 2022
Towards Explainable Evaluation Metrics for Natural Language Generation Christoph Leiter Piyawat Lertvittayakumjorn M. Fomicheva Wei Zhao Yang Gao Steffen Eger AAML ELM 30 20 0 21 Mar 2022
The Who in XAI: How AI Background Shapes Perceptions of AI Explanations Upol Ehsan Samir Passi Q. V. Liao Larry Chan I-Hsiang Lee Michael J. Muller Mark O. Riedl 32 86 0 28 Jul 2021
Survey: Leakage and Privacy at Inference Time Marija Jegorova Chaitanya Kaul Charlie Mayor Alison Q. OÑeil Alexander Weir Roderick Murray-Smith Sotirios A. Tsaftaris PILM MIACV 28 71 0 04 Jul 2021
False perfection in machine prediction: Detecting and assessing circularity problems in machine learning Michael Hagmann Stefan Riezler 18 1 0 23 Jun 2021
Bias, Fairness, and Accountability with AI and ML Algorithms Neng-Zhi Zhou Zach Zhang V. Nair Harsh Singhal Jie Chen Agus Sudjianto FaML 21 9 0 13 May 2021
From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence David Alvarez-Melis Harmanpreet Kaur Hal Daumé Hanna M. Wallach Jennifer Wortman Vaughan FAtt 53 28 0 27 Apr 2021
How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations Sérgio Jesus Catarina Belém Vladimir Balayan João Bento Pedro Saleiro P. Bizarro João Gama 136 119 0 21 Jan 2021
Reflective-Net: Learning from Explanations Johannes Schneider Michalis Vlachos FAtt OffRL LRM 57 18 0 27 Nov 2020
Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End R. Mothilal Divyat Mahajan Chenhao Tan Amit Sharma FAtt CML 27 100 0 10 Nov 2020
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review Sahil Verma Varich Boonsanong Minh Hoang Keegan E. Hines John P. Dickerson Chirag Shah CML 26 164 0 20 Oct 2020
How Interpretable and Trustworthy are GAMs? C. Chang S. Tan Benjamin J. Lengerich Anna Goldenberg R. Caruana FAtt 22 77 0 11 Jun 2020
Neural Additive Models: Interpretable Machine Learning with Neural Nets Rishabh Agarwal Levi Melnick Nicholas Frosst Xuezhou Zhang Ben Lengerich R. Caruana Geoffrey E. Hinton 46 406 0 29 Apr 2020
An Extension of LIME with Improvement of Interpretability and Fidelity Sheng Shi Yangzhou Du Wei Fan FAtt 11 8 0 26 Apr 2020
Revealing Neural Network Bias to Non-Experts Through Interactive Counterfactual Examples Chelsea M. Myers Evan Freed Luis Fernando Laris Pardo Anushay Furqan S. Risi Jichen Zhu CML 18 12 0 07 Jan 2020
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods Dylan Slack Sophie Hilgard Emily Jia Sameer Singh Himabindu Lakkaraju FAtt AAML MLAU 35 805 0 06 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
Unrestricted Permutation forces Extrapolation: Variable Importance Requires at least One More Model, or There Is No Free Variable Importance Giles Hooker L. Mentch Siyu Zhou 37 154 0 01 May 2019
Copying Machine Learning Classifiers Irene Unceta Jordi Nin O. Pujol 14 18 0 05 Mar 2019
Optimal Piecewise Local-Linear Approximations Kartik Ahuja W. Zame M. Schaar FAtt 27 1 0 27 Jun 2018
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 207 2,092 0 24 Oct 2016