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Machine learning models are increasingly used in societal applications, yet legal and privacy concerns demand that they very often be kept confidential.
The knowledge complexity of interactive proof-systems
S Goldwasser, S Micali, and C Rackoff · 1985
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Proofs that yield nothing but their validity or all languages in np have zero-knowledge proof systems
Oded Goldreich, Silvio Micali, and Avi Wigderson · 1991
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Statlog (German Credit Data)
Hans Hofmann · 1994
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Are emily and greg more employable than lakisha and jamal? a field experiment on labor market discrimination
Marianne Bertrand and Sendhil Mullainathan · 2004
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Evaluating the predictive validity of the compas risk and needs assessment system
Tim Brennan, William Dieterich, and Beate Ehret · 2009
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Consumer credit-risk models via machine-learning algorithms
Amir E. Khandani, Adlar J. Kim, and Andrew W. Lo · 2010
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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Automated experiments on ad privacy settings: A tale of opacity, choice, and discrimination
Amit Datta, Michael Carl Tschantz, and Anupam Datta · 2014
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Machine bias
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2016
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default of credit card clients
I-Cheng Yeh · 2016
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Fairsquare: Probabilistic verification of program fairness
Aws Albarghouthi, Loris D’Antoni, Samuel Drews, and Aditya V. Nori · 2017
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Oblivious neural network predictions via minionn transformations
Jian Liu, Mika Juuti, Yao Lu, and Nadarajah Asokan · 2017
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Secureml: A system for scalable privacy-preserving machine learning
Payman Mohassel and Yupeng Zhang · 2017
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Amazon scraps secret ai recruiting tool that showed bias against women, October 2018
J Dastin · 2018
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{ \{ GAZELLE } \} : A low latency framework for secure neural network inference
Chiraag Juvekar, Vinod Vaikuntanathan, and Anantha Chandrakasan · 2018
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Blind justice: Fairness with encrypted sensitive attributes
Niki Kilbertus, Adria Gascon, Matt Kusner, Michael Veale, Krishna Gummadi, and Adrian Weller · 2018
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Aby3: A mixed protocol framework for machine learning
Payman Mohassel and Peter Rindal · 2018
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Bounding and counting linear regions of deep neural networks
Thiago Serra, Christian Tjandraatmadja, and Srikumar Ramalingam · 2018
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Fairness and Machine Learning: Limitations and Opportunities
Solon Barocas, Moritz Hardt, and Arvind Narayanan · 2019
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Probabilistic verification of fairness properties via concentration
Osbert Bastani, Xin Zhang, and Armando Solar-Lezama · 2019
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Provable robustness of relu networks via maximization of linear regions
Francesco Croce, Maksym Andriushchenko, and Matthias Hein · 2019
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Faking fairness via stealthily biased sampling, 2019
Kazuto Fukuchi, Satoshi Hara, and Takanori Maehara · 2019
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Deep relu networks have surprisingly few activation patterns
Boris Hanin and David Rolnick · 2019
Cited alongside, same era.
Provable Certificates for Adversarial Examples: Fitting a Ball in the Union of Polytopes
Matt Jordan, Justin Lewis, and Alexandros G. Dimakis · 2019
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Dissecting deep neural networks
Haakon Robinson, Adil Rasheed, and Omer San · 2019
Cited alongside, same era.
Delphi: A cryptographic inference service for neural networks
Wenting Zheng Srinivasan, PMRL Akshayaram, and Popa Raluca Ada · 2019
Cited alongside, same era.
Apple card investigated after gender discrimination complaints., November, 2019
N Vigdor · 2019
Cited alongside, same era.
Mp2ml: A mixed-protocol machine learning framework for private inference
Individual fairness guarantees for neural networks
Elias Benussi, Andrea Patané, Matthew Wicker, Luca Laurenti, Marta Kwiatkowska University of Oxford, and Tu Delft · 2022
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Distrust of artificial intelligence: Sources & responses from computer science & law
Cynthia Dwork and Martha Minow · 2022
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Certifair: A framework for certified global fairness of neural networks, 2022
Haitham Khedr and Yasser Shoukry · 2022
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Fairness audit of machine learning models with confidential computing
Saerom Park, Seongmin Kim, and Yeon-sup Lim · 2022
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Privfair: a library for privacy-preserving fairness auditing, 2022
Sikha Pentyala, David Melanson, Martine De Cock, and Golnoosh Farnadi · 2022
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A learning-theoretic framework for certified auditing with explanations, 2022
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Fabian Boemer, Rosario Cammarota, Daniel Demmler, Thomas Schneider, and Hossein Yalame · 2020
Cited alongside, same era.
Justicia: A stochastic sat approach to formally verify fairness
Bishwamittra Ghosh, D. Basu, and Kuldeep S. Meel · 2020
Cited alongside, same era.
Verifying individual fairness in machine learning models, 2020
Philips George John, Deepak Vijaykeerthy, and Diptikalyan Saha · 2020
Cited alongside, same era.
vcnn: Verifiable convolutional neural network
Seunghwan Lee, Hankyung Ko, Jihye Kim, and Hyunok Oh · 2020
Cited alongside, same era.
Learning certified individually fair representations
Anian Ruoss, Mislav Balunovic, Marc Fischer, and Martin Vechev · 2020
Cited alongside, same era.
Perfectly parallel fairness certification of neural networks
Caterina Urban, Maria Christakis, Valentin Wüstholz, and Fuyuan Zhang · 2020
Cited alongside, same era.
Training individually fair ml models with sensitive subspace robustness
Mikhail Yurochkin, Amanda Bower, and Yuekai Sun · 2020
Cited alongside, same era.
Chhavi Yadav, Michal Moshkovitz, and Kamalika Chaudhuri · 2022
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Active fairness auditing, 2022
Tom Yan and Chicheng Zhang · 2022
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https://github.com/lyronctk/zator/tree/main , 2023
Zator: Verified inference of a 512-layer neural network using recursive snarks · 2023
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Fairify: Fairness verification of neural networks
Sumon Biswas and Hridesh Rajan · 2023
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Consensys/gnark: v0.9.0, February 2023
Gautam Botrel, Thomas Piellard, Youssef El Housni, Ivo Kubjas, and Arya Tabaie · 2023
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Individual fairness in bayesian neural networks, 2023
Alice Doherty, Matthew Wicker, Luca Laurenti, and Andrea Patane · 2023
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Experimenting with zero-knowledge proofs of training
Sanjam Garg, Aarushi Goel, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Guru-Vamsi Policharla, and Mingyuan Wang · 2023
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Sigma: secure gpt inference with function secret sharing
Kanav Gupta, Neha Jawalkar, Ananta Mukherjee, Nishanth Chandran, Divya Gupta, Ashish Panwar, and Rahul Sharma · 2023
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Can querying for bias leak protected attributes? achieving privacy with smooth sensitivity
Faisal Hamman, Jiahao Chen, and Sanghamitra Dutta · 2023
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Online fairness auditing through iterative refinement
Pranav Maneriker, Codi Burley, and Srinivasan Parthasarathy · 2023
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Confidential proof of fair training of trees
Ali Shahin Shamsabadi, Sierra Calanda Wyllie, Nicholas Franzese, Natalie Dullerud, Sébastien Gambs, Nicolas Papernot, Xiao Wang, and Adrian Weller · 2023
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Keeping up with the language models: Robustness-bias interplay in nli data and models
Ioana Baldini Soares, Chhavi Yadav, Payel Das, and Kush Varshney · 2023
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zkdl: Efficient zero-knowledge proofs of deep learning training, 2023
Haochen Sun and Hongyang Zhang · 2023
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Verifiable fairness: Privacy-preserving computation of fairness for machine learning systems
Ehsan Toreini, Maryam Mehrnezhad, and Aad van Moorsel · 2023
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Pvcnn: Privacy-preserving and verifiable convolutional neural network testing
Jiasi Weng, Jian Weng, Gui Tang, Anjia Yang, Ming Li, and Jia-Nan Liu · 2023
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Black-box access is insufficient for rigorous ai audits
Stephen Casper, Carson Ezell, Charlotte Siegmann, Noam Kolt, Taylor Lynn Curtis, Benjamin Bucknall, Andreas Haupt, Kevin Wei, Jérémy Scheurer, Marius Hobbhahn, et al · 2024
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