Muhammad Bilal Zafar

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Krishna P. GummadiHead, Networked Systems Group, MPI-SWSDirección de correo verificada de mpi-sws.org
Manuel Gomez RodriguezFaculty, Max Planck Institute for Software Systems (MPI-SWS)Dirección de correo verificada de mpi-sws.org
Isabel ValeraSaarland University and Max Planck Institute for Software SystemsDirección de correo verificada de cs.uni-saarland.de
Adrian WellerDirector of Research, Machine Learning, University of CambridgeDirección de correo verificada de eng.cam.ac.uk
Michele DoniniAmazonDirección de correo verificada de amazon.com
Saptarshi GhoshDepartment of CSE, Indian Institute of Technology Kharagpur, IndiaDirección de correo verificada de cse.iitkgp.ac.in
Krishnaram KenthapadiFiddler AIDirección de correo verificada de CS.Stanford.EDU
Niloy GangulyDepartment of CSE, Indian Institute of Technology, KharagpurDirección de correo verificada de cse.iitkgp.ac.in
Parantapa BhattacharyaResearch Scientist, Biocomplexity Institute & Initiative, University of VirginiaDirección de correo verificada de virginia.edu
Sanjiv DasSanta Clara UniversityDirección de correo verificada de scu.edu
Juhi KulshresthaDepartment of Computer Science, Aalto UniversityDirección de correo verificada de aalto.fi
Cédric ArchambeauHelsing, BerlinDirección de correo verificada de helsing.ai
Till SpeicherPhD student, Planck Institute for Software Systems (MPI-SWS)Dirección de correo verificada de mpi-sws.org
Motahhare EslamiCarnegie Mellon UniversityDirección de correo verificada de andrew.cmu.edu
Karrie KarahaliosUniversity of IllinoisDirección de correo verificada de cs.uiuc.edu
Adish SinglaTenure-track Faculty, Max Planck Institute for Software Systems (MPI-SWS), GermanyDirección de correo verificada de mpi-sws.org
Robin SchmuckerCarnegie Mellon UniversityDirección de correo verificada de cs.cmu.edu
Aniket KatePurdue University / Supra ResearchDirección de correo verificada de purdue.edu
Hoda HeidariCarnegie Mellon UniversityDirección de correo verificada de cmu.edu
Valerio PerroneApplied Science Manager, AmazonDirección de correo verificada de amazon.com

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Muhammad Bilal Zafar

Ruhr University Bochum & Research Center for Trustworthy Data Science and Security

Dirección de correo verificada de rub.de - Página principal

Algorithmic Fairness Interpretability Ethical AI


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Fairness Constraints: Mechanisms for Fair Classification MB Zafar, I Valera, MG Rodriguez, KP Gummadi 20th International Conference on Artificial Intelligence and Statistics …, 2017	1333	2017
Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment MB Zafar, I Valera, MG Rodriguez, KP Gummadi 26th International World Wide Web Conference (WWW), 2017	1272	2017
Fairness constraints: A flexible approach for fair classification MB Zafar, I Valera, M Gomez-Rodriguez, KP Gummadi Journal of Machine Learning Research 20 (75), 1-42, 2019	358	2019
A unified approach to quantifying algorithmic unfairness: Measuring individual &group unfairness via inequality indices T Speicher, H Heidari, N Grgic-Hlaca, KP Gummadi, A Singla, A Weller, ... Proceedings of the 24th ACM SIGKDD international conference on knowledge …, 2018	302	2018
Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media J Kulshrestha, M Eslami, J Messias, MB Zafar, S Ghosh, I Shibpur, ... 20th ACM Conference on Computer-Supported Cooperative Work and Social …, 2017	259	2017
From Parity to Preference-based Notions of Fairness in Classification MB Zafar, I Valera, MG Rodriguez, KP Gummadi, A Weller 31st Conference on Neural Information Processing Systems (NIPS), 2017	253	2017
The Case for Process Fairness in Learning: Feature Selection for Fair Decision Making N Grgic-Hlaca, MB Zafar, KP Gummadi, A Weller NIPS Symposium on ML and the Law, 2016	240	2016
Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning N Grgic-Hlaca, MB Zafar, KP Gummadi, A Weller 32nd AAAI Conference on Artificial Intelligence (AAAI-18), 2018	224	2018
Inferring User Interests in the Twitter Social Network P Bhattacharya, MB Zafar, N Ganguly, S Ghosh, KP Gummadi 8th ACM Conference on Recommender Systems (RecSys), 2014	155	2014
Search bias quantification: investigating political bias in social media and web search J Kulshrestha, M Eslami, J Messias, MB Zafar, S Ghosh, KP Gummadi, ... Information Retrieval Journal 22, 188-227, 2019	119	2019
On Sampling the Wisdom of Crowds: Random vs. Expert Sampling of the Twitter Stream S Ghosh, MB Zafar, P Bhattacharya, N Sharma, N Ganguly, K Gummadi 22nd ACM International Conference on Information & Knowledge Management …, 2013	90	2013
Listening to Whispers of Ripple: Linking Wallets and Deanonymizing Transactions in the Ripple Network P Moreno-Sanchez, MB Zafar, A Kate Privacy Enhancing Technologies Symposium (PETS), 2016	85	2016
Fair bayesian optimization V Perrone, M Donini, MB Zafar, R Schmucker, K Kenthapadi, ... Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 854-863, 2021	73	2021
Strength in Numbers: Robust Tamper Detection in Crowd Computations B Viswanath, MA Bashir, MB Zafar, S Bouget, S Guha, KP Gummadi, ... 2015 ACM Conference on Online Social Networks (COSN), 2015	57	2015
Deep Twitter Diving: Exploring Topical Groups in Microblogs at Scale P Bhattacharya, S Ghosh, J Kulshrestha, M Mondal, MB Zafar, N Ganguly, ... 17th ACM Conference on Computer-Supported Cooperative Work & Social …, 2014	55	2014
Sampling Content from Online Social Networks: Comparing Random vs. Expert Sampling of the Twitter Stream MB Zafar, P Bhattacharya, N Ganguly, KP Gummadi, S Ghosh ACM Transactions on the Web (TWEB), 2015	52	2015
Characterizing Information Diets of Social Media Users J Kulshrestha, MB Zafar, LE Noboa, KP Gummadi, S Ghosh 9th International AAAI Conference on Web and Social Media (ICWSM), 2015	51	2015
On Fairness, Diversity and Randomness in Algorithmic Decision Making N Grgić-Hlača, MB Zafar, KP Gummadi, A Weller 4th Workshop on Fairness, Accountability, and Transparency in Machine …, 2017	39	2017
Amazon sagemaker clarify: Machine learning bias detection and explainability in the cloud M Hardt, X Chen, X Cheng, M Donini, J Gelman, S Gollaprolu, J He, ... Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021	36	2021
Amazon sagemaker model monitor: A system for real-time insights into deployed machine learning models D Nigenda, Z Karnin, MB Zafar, R Ramesha, A Tan, M Donini, ... Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022	33	2022

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