Fetching the paper…
Reading the bibliography…
Black-box machine learning models are being used in more and more high-stakes domains, which creates a growing need for Explainable AI (XAI).
Greedy randomized adaptive search procedures
Thomas A Feo and Mauricio GC Resende · 1995
Earlier work this paper cites.
Simple demographics often identify people uniquely
Latanya Sweeney · 2000
Earlier work this paper cites.
Transforming data to satisfy privacy constraints
Vijay S Iyengar · 2002
Earlier work this paper cites.
On the complexity of optimal k-anonymity
Adam Meyerson and Ryan Williams · 2004
Earlier work this paper cites.
Data privacy through optimal k-anonymization
Roberto J Bayardo and Rakesh Agrawal · 2005
Earlier work this paper cites.
Incognito: Efficient full-domain k-anonymity
Kristen LeFevre, David J DeWitt, and Raghu Ramakrishnan · 2005
Earlier work this paper cites.
Fast data anonymization with low information loss
Gabriel Ghinita, Panagiotis Karras, Panos Kalnis, and Nikos Mamoulis · 2007
Earlier work this paper cites.
k-anonymization with minimal loss of information
Aristides Gionis and Tamir Tassa · 2008
Earlier work this paper cites.
Protecting privacy using k-anonymity
Khaled El Emam and Fida Kamal Dankar · 2008
Earlier work this paper cites.
kactus 2: Privacy preserving in classification tasks using k-anonymity
Slava Kisilevich, Yuval Elovici, Bracha Shapira, and Lior Rokach · 2008
Earlier work this paper cites.
A framework for efficient data anonymization under privacy and accuracy constraints
Gabriel Ghinita, Panagiotis Karras, Panos Kalnis, and Nikos Mamoulis · 2009
Earlier work this paper cites.
Utility-based k-anonymization
Qingming Tang, Yinjie Wu, Shangbin Liao, and Xiaodong Wang · 2010
Earlier work this paper cites.
Anonymity: an assessment and perspective in privacy preserving data mining
M Sumana and KS Hareesha · 2010
Earlier work this paper cites.
Utility-preserving transaction data anonymization with low information loss
Grigorios Loukides and Aris Gkoulalas-Divanis · 2012
Earlier work this paper cites.
Explaining data-driven document classifications
David Martens and Foster Provost · 2014
Cited alongside, same era.
Encyclopedia of cryptography and security
Henk CA Van Tilborg and Sushil Jajodia · 2014
Cited alongside, same era.
A systematic comparison and evaluation of k-anonymization algorithms for practitioners
Vanessa Ayala-Rivera, Patrick McDonagh, Thomas Cerqueus, Liam Murphy, et al · 2014
Cited alongside, same era.
Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
Cited alongside, same era.
Counterfactual explanations without opening the black box: Automated decisions and the gdpr
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2017
Cited alongside, same era.
An extensive study on data anonymization algorithms based on k-anonymity
MS Simi, K Sankara Nayaki, and M Sudheep Elayidom · 2017
A survey of privacy attacks in machine learning
Maria Rigaki and Sebastian Garcia · 2020
Later among the works it cites.
Model explanations with differential privacy
Neel Patel, Reza Shokri, and Yair Zick · 2020
Later among the works it cites.
Good counterfactuals and where to find them: A case-based technique for generating counterfactuals for explainable ai (xai)
Mark T Keane and Barry Smyth · 2020
Later among the works it cites.
On counterfactual explanations under predictive multiplicity
Martin Pawelczyk, Klaus Broelemann, and Gjergji Kasneci · 2020
Later among the works it cites.
Face: feasible and actionable counterfactual explanations
Rafael Poyiadzi, Kacper Sokol, Raul Santos-Rodriguez, Tijl De Bie, and Peter Flach · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Anatomization through generalization (ag): A hybrid privacy-preserving approach to prevent membership, identity and semantic similarity disclosure attacks
Rashad Saeed and Azhar Rauf · 2018
Cited alongside, same era.
Counterfactual explanations of machine learning predictions: opportunities and challenges for ai safety
Kacper Sokol and Peter A Flach · 2019
Cited alongside, same era.
The dangers of post-hoc interpretability: Unjustified counterfactual explanations
Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, and Marcin Detyniecki · 2019
Cited alongside, same era.
The what-if tool: Interactive probing of machine learning models
James Wexler, Mahima Pushkarna, Tolga Bolukbasi, Martin Wattenberg, Fernanda Viégas, and Jimbo Wilson · 2019
Cited alongside, same era.
Race, ethnicity, gender, & class : the sociology of group conflict and change
Joseph F. Healey, Andi Stepnick, and Eileen O’Brien · 2019
Cited alongside, same era.
Privacy risks of explaining machine learning models
Reza Shokri, Martin Strobel, and Yair Zick · 2019
Cited alongside, same era.
When machine learning meets privacy: A survey and outlook
Bo Liu, Ming Ding, Sina Shaham, Wenny Rahayu, Farhad Farokhi, and Zihuai Lin · 2021
Later among the works it cites.
Trade-offs between privacy-preserving and explainable machine learning in healthcare
Tobias Budig, Selina Herrmann, and Alexander Dietz · 2021
Later among the works it cites.
Nice: an algorithm for nearest instance counterfactual explanations
Dieter Brughmans and David Martens · 2021
Later among the works it cites.
Evaluating robustness of counterfactual explanations
André Artelt, Valerie Vaquet, Riza Velioglu, Fabian Hinder, Johannes Brinkrolf, Malte Schilling, and Barbara Hammer · 2021
Later among the works it cites.
k-anonymity in practice: How generalisation and suppression affect machine learning classifiers
Djordje Slijepčević, Maximilian Henzl, Lukas Daniel Klausner, Tobias Dam, Peter Kieseberg, and Matthias Zeppelzauer · 2021
Later among the works it cites.
A framework and benchmarking study for counterfactual generating methods on tabular data
Raphael Mazzine Barbosa de Oliveira and David Martens · 2021
Later among the works it cites.
Regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts
European Commission · 2022
Closest in time.
Counterfactual explanations and how to find them: literature review and benchmarking
Riccardo Guidotti · 2022
Closest in time.