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Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey [article]

Max Hort, Zhenpeng Chen, Jie M. Zhang, Federica Sarro, Mark Harman
2022 arXiv   pre-print
This paper provides a comprehensive survey of bias mitigation methods for achieving fairness in Machine Learning (ML) models.  ...  We collect a total of 341 publications concerning bias mitigation for ML classifiers.  ...  To summarize, the contribution of this survey are: 1) we provide a comprehensive overview of the research on bias mitigation methods for ML classifiers; 2) we introduce the experimental design details  ... 
arXiv:2207.07068v3 fatcat:6lge4ldht5fcvlw3tgbxbqevtq

Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning [article]

Wencan Zhang, Mariella Dimiccoli, Brian Y. Lim
2022 arXiv   pre-print
Debiased training can provide a versatile platform for robust performance and explanation faithfulness for a wide range of applications with data biases.  ...  We present Debiased-CAM to recover explanation faithfulness across various bias types and levels by training a multi-input, multi-task model with auxiliary tasks for explanation and bias level predictions  ...  ) and the NUS Institute for Health Innovation and Technology (iHealthtech).  ... 
arXiv:2012.05567v3 fatcat:nqe2hortkrdlzd2ueuhbxmebuy

A Comprehensive Survey on Trustworthy Recommender Systems [article]

Wenqi Fan, Xiangyu Zhao, Xiao Chen, Jingran Su, Jingtong Gao, Lin Wang, Qidong Liu, Yiqi Wang, Han Xu, Lei Chen, Qing Li
2022 arXiv   pre-print
In this survey, we provide a comprehensive overview of Trustworthy Recommender systems (TRec) with a specific focus on six of the most important aspects; namely, Safety & Robustness, Nondiscrimination  ...  Therefore, systems' trustworthiness has been attracting increasing attention from various aspects for mitigating negative impacts caused by recommender systems, so as to enhance the public's trust towards  ...  Various toolkits have been developed to evaluate or mitigate bias in machine learning models.  ... 
arXiv:2209.10117v1 fatcat:p2dc3xywl5hr3eoy4alvxtqbdu

Fairness Testing: A Comprehensive Survey and Analysis of Trends [article]

Zhenpeng Chen, Jie M. Zhang, Max Hort, Federica Sarro, Mark Harman
2022 arXiv   pre-print
This paper provides a comprehensive survey of existing research on fairness testing.  ...  ., where to find fairness bugs) for conducting fairness testing.  ...  ACKNOWLEDGMENTS Before submitting, we sent the paper to the authors of the collected papers, to check for accuracy and omission.  ... 
arXiv:2207.10223v2 fatcat:2k3zj2lr2fh7dhapetkm6irame

A Survey on Measuring and Mitigating Reasoning Shortcuts in Machine Reading Comprehension [article]

Xanh Ho, Johannes Mario Meissner, Saku Sugawara, Akiko Aizawa
2022 arXiv   pre-print
In this survey paper, we focus on the field of machine reading comprehension (MRC), an important task for showcasing high-level language understanding that also suffers from a range of shortcuts.  ...  Most importantly, we highlight two main concerns for shortcut mitigation in MRC: the lack of public challenge sets, a necessary component for effective and reusable evaluation, and the lack of certain  ...  This raises the need for a survey paper. We try to summarize and classify most existing works to provide a broad-picture view for researchers on measuring and mitigating shortcuts in MRC.  ... 
arXiv:2209.01824v1 fatcat:6khjxvovizeztoevwghfirmhcu

Scaling Blockchains: A Comprehensive Survey

Abdelatif Hafid, Abdelhakim Senhaji Hafid, Mustapha Samih
2020 IEEE Access  
Interested readers are referred to [90] and [94] for comprehensive surveys of distributed consen- sus for Blockchain Networks.  ...  An interesting research direction to investigate is the use of Machine Learning (ML) algorithms to dynamically determine the size of the committee.  ... 
doi:10.1109/access.2020.3007251 fatcat:72oattr4dzfzjoxfe2vvqtvuna

Human Attribute Recognition— A Comprehensive Survey

Ehsan Yaghoubi, Farhad Khezeli, Diana Borza, SV Aruna Kumar, João Neves, Hugo Proença
2020 Applied Sciences  
HAR's main challenges; (2) we provide a comprehensive discussion over the publicly available datasets for the development and evaluation of novel HAR approaches; (3) we outline the applications and typical  ...  To provide insights for future algorithm design and dataset collections, in this survey, (1) we provide an in-depth analysis of existing HAR techniques, concerning the advances proposed to address the  ...  Therefore, the final output is a pyramid of part model scores suitable for learning an SVM classifier.  ... 
doi:10.3390/app10165608 fatcat:yrzjf2v73fabbagqfyxfuhzfla

Privacy–Enhancing Face Biometrics: A Comprehensive Survey

Blaz Meden, Peter Rot, Philipp Terhorst, Naser Damer, Arjan Kuijper, Walter J. Scheirer, Arun Ross, Peter Peer, Vitomir Struc
2021 IEEE Transactions on Information Forensics and Security  
The goal of this overview paper is to provide a comprehensive introduction into privacy-related research in the area of biometrics and review existing work on Biometric Privacy-Enhancing Techniques (B-PETs  ...  These efforts have resulted in a multitude of privacy-enhancing techniques that aim at addressing privacy risks originating from biometric systems and providing technological solutions for legislative  ...  For more information, see This article has been accepted for publication in a future issue of this journal, but has not been fully edited.  ... 
doi:10.1109/tifs.2021.3096024 fatcat:z5kvij6g7vgx3b24narxdyp2py

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Javier Mata, Ignacio de Miguel, Ramón J. Durán, Noemí Merayo, Sandeep Kumar Singh, Admela Jukan, Mohit Chamania
2018 Optical Switching and Networkning Journal  
This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks.  ...  Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.  ...  [56] propose a machine learning algorithm to mitigate NLPN affecting M-ary phase-shift keying (M-PSK) based coherent optical transmission systems.  ... 
doi:10.1016/j.osn.2017.12.006 fatcat:i443tt6fv5g2jmvfe7kn57unbq

A Comprehensive Survey for Intelligent Spam Email Detection

Asif Karim, Sami Azam, Bharanidharan Shanmugam, Krishnan Kannoorpatti, Mamoun Alazab
2019 IEEE Access  
This survey paper describes a focused literature survey of Artificial Intelligence (AI) and Machine Learning (ML) methods for intelligent spam email detection, which we believe can help in developing appropriate  ...  This comprehensive survey paves the way for future research endeavors addressing theoretical and empirical aspects related to intelligent spam email detection.  ...  for the model had been built upon a single Machine Learning algorithm.  ... 
doi:10.1109/access.2019.2954791 fatcat:ikt6cayggbb2dkrm52fxzz2dqm

Robust and Privacy-Preserving Collaborative Learning: A Comprehensive Survey [article]

Shangwei Guo, Xu Zhang, Fei Yang, Tianwei Zhang, Yan Gan, Tao Xiang, Yang Liu
2021 arXiv   pre-print
With the rapid demand of data and computational resources in deep learning systems, a growing number of algorithms to utilize collaborative machine learning techniques, for example, federated learning,  ...  Compared with existing surveys that mainly focus on one specific collaborative learning system, this survey aims to provide a systematic and comprehensive review of security and privacy researches in collaborative  ...  In this survey, machine learning is mostly referred to as supervised learning.  ... 
arXiv:2112.10183v1 fatcat:ujfz4a5mdrhsbk4kiqoqo2snfe

Adversarial Examples on Object Recognition: A Comprehensive Survey [article]

Alex Serban, Erik Poll, Joost Visser
2020 arXiv   pre-print
Altogether, the goal is to provide a comprehensive and self-contained survey of this growing field of research.  ...  Deep neural networks are at the forefront of machine learning research.  ...  The goal of this paper is to provide a comprehensive survey of this research field.  ... 
arXiv:2008.04094v2 fatcat:7xycyybhpvhshawt7fy3fzeana

Machine Learning for Security in Vehicular Networks: A Comprehensive Survey [article]

Anum Talpur, Mohan Gurusamy
2021 arXiv   pre-print
In this paper, we present a comprehensive survey of ML-based techniques for different security issues in vehicular networks.  ...  Machine Learning (ML) has emerged as an attractive and viable technique to provide effective solutions for a wide range of application domains.  ...  and Longitudinal Vehicle Control System Autonomous Vehicle Network ML and DL Our Work 2021 Machine Learning for Security in Vehicular Networks: A Comprehensive Survey Security, Trust and Privacy  ... 
arXiv:2105.15035v2 fatcat:5z6aqlvosjgf3o3amts3k6toxu

A comprehensive survey on safe reinforcement learning

Javier García, Fernando Fernández
2015 Journal of machine learning research  
We use the proposed classification to survey the existing literature, as well as suggesting future directions for Safe Reinforcement Learning.  ...  The first is based on the modification of the optimality criterion, the classic discounted finite/infinite horizon, with a safety factor.  ...  In this survey, we focus on step one to classify the different approaches of this trend.  ... 
dblp:journals/jmlr/GarciaF15 fatcat:sl4to7d7bvbt7pzsrnodubhdhy

Twenty-two years since revealing cross-site scripting attacks: a systematic mapping and a comprehensive survey [article]

Abdelhakim Hannousse and Salima Yahiouche and Mohamed Cherif Nait-Hamoud
2022 arXiv   pre-print
In this paper, we contribute by conducting a systematic mapping and a comprehensive survey.  ...  Although the diversity of XSS attack types and the scripting languages that can be used to state them, the systematic mapping revealed a remarkable bias toward basic and JavaScript XSS attacks and a dearth  ...  Feature vectors are built and fed to machine learning classifiers for training and then for classifying web pages into vulnerable and safe.  ... 
arXiv:2205.08425v2 fatcat:mz2upyb3d5ekllmw66t7s4rsom
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