Counterfactual Fairness

20 March 2017

Papers citing "Counterfactual Fairness"

50 / 823 papers shown

Title
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach Amir-Hossein Karimi Julius von Kügelgen Bernhard Schölkopf Isabel Valera CML 28 178 0 11 Jun 2020
Deep Structural Causal Models for Tractable Counterfactual Inference Nick Pawlowski Daniel Coelho De Castro Ben Glocker CML MedIm 33 232 0 11 Jun 2020
A Variational Approach to Privacy and Fairness Borja Rodríguez Gálvez Ragnar Thobaben Mikael Skoglund FaML DRL 19 25 0 11 Jun 2020
Causal Feature Selection for Algorithmic Fairness Sainyam Galhotra Karthikeyan Shanmugam P. Sattigeri Kush R. Varshney FaML 28 39 0 10 Jun 2020
On Low Rank Directed Acyclic Graphs and Causal Structure Learning Zhuangyan Fang Shengyu Zhu Jiji Zhang Yue Liu Zhitang Chen Yangbo He CML 23 27 0 10 Jun 2020
Probably Approximately Correct Constrained Learning Luiz F. O. Chamon Alejandro Ribeiro 22 38 0 09 Jun 2020
Achieving Equalized Odds by Resampling Sensitive Attributes Yaniv Romano Stephen Bates Emmanuel J. Candès FaML 13 48 0 08 Jun 2020
The Criminality From Face Illusion Kevin W. Bowyer Michael C. King Walter J. Scheirer Kushal Vangara CVBM 16 20 0 06 Jun 2020
Fairness-Aware Explainable Recommendation over Knowledge Graphs Zuohui Fu Yikun Xian Ruoyuan Gao Jieyu Zhao Qiaoying Huang ... Shuyuan Xu Shijie Geng C. Shah Yongfeng Zhang Gerard de Melo FaML 12 204 0 03 Jun 2020
What's Sex Got To Do With Fair Machine Learning? Lily Hu Issa Kohler-Hausmann FaML 19 80 0 02 Jun 2020
Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression Domagoj Cevid Loris Michel Jeffrey Näf N. Meinshausen Peter Buhlmann 35 39 0 29 May 2020
Review of Mathematical frameworks for Fairness in Machine Learning E. del Barrio Paula Gordaliza Jean-Michel Loubes FaML FedML 15 39 0 26 May 2020
Gender Slopes: Counterfactual Fairness for Computer Vision Models by Attribute Manipulation Jungseock Joo Kimmo Karkkainen 18 48 0 21 May 2020
Principal Fairness for Human and Algorithmic Decision-Making Kosuke Imai Zhichao Jiang FaML 29 30 0 21 May 2020
Towards classification parity across cohorts Aarsh Patel Rahul Gupta Mukund Sridhar Satyapriya Krishna Aman Alok Peng Liu FaML 33 1 0 16 May 2020
Statistical Equity: A Fairness Classification Objective Ninareh Mehrabi Yuzhong Huang Fred Morstatter FaML 20 10 0 14 May 2020
Deep Learning for Political Science Kakia Chatsiou Slava Jankin AI4CE 39 12 0 13 May 2020
A Ladder of Causal Distances Maxime Peyrard Robert West CML 16 6 0 05 May 2020
Stereotype-Free Classification of Fictitious Faces Mohammadhossein Toutiaee Soheyla Amirian John A. Miller Sheng Li CVBM 16 6 0 29 Apr 2020
The Impact of Presentation Style on Human-In-The-Loop Detection of Algorithmic Bias Po-Ming Law Sana Malik F. Du Moumita Sinha 34 6 0 26 Apr 2020
Shortcut Learning in Deep Neural Networks Robert Geirhos J. Jacobsen Claudio Michaelis R. Zemel Wieland Brendel Matthias Bethge Felix Wichmann 55 1,996 0 16 Apr 2020
Abstracting Fairness: Oracles, Metrics, and Interpretability Cynthia Dwork Christina Ilvento G. Rothblum Pragya Sur FaML 25 5 0 04 Apr 2020
A survey of bias in Machine Learning through the prism of Statistical Parity for the Adult Data Set Philippe C. Besse E. del Barrio Paula Gordaliza Jean-Michel Loubes Laurent Risser FaML 22 63 0 31 Mar 2020
Fair inference on error-prone outcomes L. Boeschoten E. V. Kesteren A. Bagheri Daniel L. Oberski 11 2 0 17 Mar 2020
Fairness by Explicability and Adversarial SHAP Learning James M. Hickey Pietro G. Di Stefano V. Vasileiou FAtt FedML 33 19 0 11 Mar 2020
Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation Raha Moraffah Mansooreh Karami Ruocheng Guo A. Raglin Huan Liu CML ELM XAI 29 213 0 09 Mar 2020
Detection and Mitigation of Bias in Ted Talk Ratings Rupam Acharyya Shouman Das Ankani Chattoraj Oishani Sengupta Md Iftekar Tanveer CML 27 3 0 02 Mar 2020
Unbiased Scene Graph Generation from Biased Training Kaihua Tang Yulei Niu Jianqiang Huang Jiaxin Shi Hanwang Zhang CML 22 682 0 27 Feb 2020
Counterfactual fairness: removing direct effects through regularization Pietro G. Di Stefano James M. Hickey V. Vasileiou FaML 17 19 0 25 Feb 2020
Learning Certified Individually Fair Representations Anian Ruoss Mislav Balunović Marc Fischer Martin Vechev FaML 15 92 0 24 Feb 2020
FR-Train: A Mutual Information-Based Approach to Fair and Robust Training Yuji Roh Kangwook Lee Steven Euijong Whang Changho Suh 24 78 0 24 Feb 2020
Robust Optimization for Fairness with Noisy Protected Groups S. Wang Wenshuo Guo Harikrishna Narasimhan Andrew Cotter Maya R. Gupta Michael I. Jordan NoLa 27 118 0 21 Feb 2020
Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint Yoichi Chikahara Shinsaku Sakaue Akinori Fujino Hisashi Kashima FaML 16 0 0 17 Feb 2020
Convex Fairness Constrained Model Using Causal Effect Estimators Hikaru Ogura Akiko Takeda 6 2 0 16 Feb 2020
Algorithmic Recourse: from Counterfactual Explanations to Interventions Amir-Hossein Karimi Bernhard Schölkopf Isabel Valera CML 24 337 0 14 Feb 2020
CheXclusion: Fairness gaps in deep chest X-ray classifiers Laleh Seyyed-Kalantari Guanxiong Liu Matthew B. A. McDermott Irene Y. Chen Marzyeh Ghassemi OOD 37 285 0 14 Feb 2020
To Split or Not to Split: The Impact of Disparate Treatment in Classification Hao Wang Hsiang Hsu Mario Díaz Flavio du Pin Calmon 15 23 0 12 Feb 2020
Oblivious Data for Fairness with Kernels Steffen Grunewalder A. Khaleghi 16 6 0 07 Feb 2020
A Survey on Causal Inference Liuyi Yao Zhixuan Chu Sheng Li Yaliang Li Jing Gao Aidong Zhang CML 29 501 0 05 Feb 2020
Deontological Ethics By Monotonicity Shape Constraints S. Wang Maya R. Gupta 21 21 0 31 Jan 2020
One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency Kacper Sokol Peter A. Flach 6 173 0 27 Jan 2020
Privacy for All: Demystify Vulnerability Disparity of Differential Privacy against Membership Inference Attack Bo Zhang Ruotong Yu Haipei Sun Yanying Li Jun Xu Wendy Hui Wang AAML 22 13 0 24 Jan 2020
Algorithmic Fairness Dana Pessach E. Shmueli FaML 33 386 0 21 Jan 2020
Adequate and fair explanations Nicholas M. Asher Soumya Paul Chris Russell 35 9 0 21 Jan 2020
The Incentives that Shape Behaviour Ryan Carey Eric D. Langlois Tom Everitt Shane Legg CML 25 13 0 20 Jan 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
Fair Active Learning Hadis Anahideh Abolfazl Asudeh Saravanan Thirumuruganathan FaML 46 51 0 06 Jan 2020
Learning from Discriminatory Training Data Przemyslaw A. Grabowicz Nicholas Perello Kenta Takatsu FaML 22 1 0 17 Dec 2019
Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics Debjani Saha Candice Schumann Duncan C. McElfresh John P. Dickerson Michelle L. Mazurek Michael Carl Tschantz FaML 32 16 0 17 Dec 2019
Artificial mental phenomena: Psychophysics as a framework to detect perception biases in AI models Lizhen Liang Daniel Ernesto Acuna 22 13 0 15 Dec 2019