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In a world of increasing closed-source commercial machine learning models, model evaluations from developers must be taken at face value.
Robust linear programming discrimination of two linearly inseparable sets
K. P. Bennett and O. L. Mangasarian · 1992
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Evaluating natural language processing systems: An analysis and review
Karen Sparck Jones and Julia R Galliers · 1995
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Constant-size commitments to polynomials and their applications
Aniket Kate, Gregory M Zaverucha, and Ian Goldberg · 2010
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The mnist database of handwritten digit images for machine learning research
Li Deng · 2012
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
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Long short-term memory recurrent neural network architectures for large scale acoustic modeling
Hasim Sak, Andrew W Senior, and Françoise Beaufays · 2014
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Accountable algorithms
Joshua Alexander Kroll · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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On the size of pairing-based non-interactive arguments
Jens Groth · 2016
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Auditing algorithms for discrimination
Pauline T Kim · 2017
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Trust but verify: A guide to algorithms and the law
Deven R Desai and Joshua A Kroll · 2017
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Safetynets: Verifiable execution of deep neural networks on an untrusted cloud
Zahra Ghodsi, Tianyu Gu, and Siddharth Garg · 2017
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
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Help wanted: An examination of hiring algorithms, equity, and bias
Miranda Bogen and Aaron Rieke · 2018
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Understanding unequal gender classification accuracy from face images
Vidya Muthukumar, Tejaswini Pedapati, Nalini K. Ratha, Prasanna Sattigeri, Chai-Wah Wu, Brian Kingsbury, Abhishek Kumar, Samuel Thomas, Aleksandra Mojsilovic, and Kush R. Varshney · 2018
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The measure and mismeasure of fairness: A critical review of fair machine learning
Sam Corbett-Davies and Sharad Goel · 2018
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Updatable and universal common reference strings with applications to zk-snarks
Jens Groth, Markulf Kohlweiss, Mary Maller, Sarah Meiklejohn, and Ian Miers · 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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Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial ai products
Inioluwa Deborah Raji and Joy Buolamwini · 2019
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Model cards for model reporting
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru · 2019
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A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Ani Saxena, Kristina Lerman, and A. G. Galstyan · 2019
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On measuring social biases in sentence encoders
Chandler May, Alex Wang, Shikha Bordia, Samuel R. Bowman, and Rachel Rudinger · 2019
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Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial ai products
Inioluwa Deborah Raji and Joy Buolamwini · 2019
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Compiling classical ml pipelines into tensor computations for one-size-fits-all prediction serving
Supun Nakandala, Gyeong-In Yu, Markus Weimer, and Matteo Interlandi · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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A framework for understanding sources of harm throughout the machine learning life cycle
Harini Suresh and John V. Guttag · 2019
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Assessing social and intersectional biases in contextualized word representations
Yi Chern Tan and L Elisa Celis · 2019
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Predictive biases in natural language processing models: A conceptual framework and overview
Deven Shah, H Andrew Schwartz, and Dirk Hovy · 2019
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D elphi : A cryptographic inference service for neural networks
Pratyush Mishra, Ryan T. Lehmkuhl, Akshayaram Srinivasan, Wenting Zheng, and Raluca A. Popa · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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vcnn: Verifiable convolutional neural network based on zk-snarks
Seunghwan Lee · 2020
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wav2vec 2.0: A framework for self-supervised learning of speech representations
Alexei Baevski, Yuhao Zhou, Abdelrahman Mohamed, and Michael Auli · 2020
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, T. J. Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeff Wu, and Dario Amodei · 2020
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Holistic evaluation of language models
Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, et al · 2022
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Benchmarking intersectional biases in nlp
John P. Lalor, Yi Yang, Kendall Smith, Nicole Forsgren, and Ahmed Abbasi · 2022
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Towards a standard for identifying and managing bias in artificial intelligence
Reva Schwartz, Apostol T. Vassilev, Kristen Greene, Lori A. Perine, Andrew Burt, and Patrick Hall · 2022
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The halo2 book, 2022
zcash · 2022
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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 · 2022
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An intersectional definition of fairness
James R Foulds, Rashidul Islam, Kamrun Naher Keya, and Shimei Pan · 2020
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Language (technology) is power: A critical survey of" bias" in nlp
Su Lin Blodgett, Solon Barocas, Hal Daumé III, and Hanna Wallach · 2020
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Halo infinite: Recursive zk-snarks from any additive polynomial commitment scheme
Dan Boneh, Justin Drake, Ben Fisch, and Ariel Gabizon · 2020
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Spartan: Efficient and general-purpose zksnarks without trusted setup
Srinath Setty · 2020
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Artificial intelligence act, 4 2021
European Commission · 2021
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Improving reproducibility in machine learning research (a report from the neurips 2019 reproducibility program)
Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Larivière, Alina Beygelzimer, Florence d’Alché Buc, Emily Fox, and Hugo Larochelle · 2021
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Fairface: Face attribute dataset for balanced race, gender, and age for bias measurement and mitigation
Kimmo Kärkkäinen and Jungseock Joo · 2021
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Ulrich Haböck · 2022
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Nova: Recursive zero-knowledge arguments from folding schemes
Abhiram Kothapalli, Srinath Setty, and Ioanna Tzialla · 2022
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Securing artificial intelligence model weights: Interim report
Ajay Karpur, Dan Lahav, Jason Matheny, Jeff Alstott, and Sella Nevo · 2023
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How is chatgpt’s behavior changing over time?
Lingjiao Chen, Matei Zaharia, and James Zou · 2023
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Executive order on the safe, secure, and trustworthy development and use of artificial intelligence, 10 2023
The White House · 2023
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Reproducibility in machine learning-driven research
Harald Semmelrock, Simone Kopeinik, Dieter Theiler, Tony Ross-Hellauer, and Dominik Kowald · 2023
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Reproducibility of machine learning: Terminology, recommendations and open issues
Riccardo Albertoni, Sara Colantonio, Piotr Skrzypczy’nski, and Jerzy Stefanowski · 2023
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Anya Belz, Craig Thomson, Ehud Reiter, Gavin Abercrombie, Jose Maria Alonso-Moral, Mohammad Arvan, Jackie Chi Kit Cheung, Mark Cieliebak, Elizabeth Clark, Kees van Deemter, Tanvi Dinkar, Ondrej Dusek, Steffen Eger, Qixiang Fang, Albert Gatt, Dimitra Gkatzia, Javier Gonz’alez-Corbelle, Dirk Hovy, Manuela Hurlimann, Takumi Ito, John D. Kelleher, Filip Klubicka, Huiyuan Lai, Chris van der Lee, Emiel van Miltenburg, Yiru Li, Saad Mahamood, Margot Mieskes, Malvina Nissim, Natalie Parde, Ondvrej Pl’atek, Verena Rieser, Pablo Romero, Joel Tetreault, Antonio Toral, Xiao-Yi Wan, L. Wanner, Lewis J. Watson, and Diyi Yang · 2023
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"get in researchers; we’re measuring reproducibility": A reproducibility study of machine learning papers in tier 1 security conferences
Daniel Olszewski, Allison Lu, Carson Stillman, Kevin Warren, Cole Kitroser, Alejandro Pascual, Divyajyoti Ukirde, Kevin Butler, and Patrick Traynor · 2023
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Faircompass: Operationalising fairness in machine learning
Jessica Liu, Huaming Chen, Jun Shen, and Kim-Kwang Raymond Choo · 2023
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Fairness in deep learning: A survey on vision and language research
Otávio Parraga, Martin D. Móre, Christian Mattjie de Oliveira, Nathan S. Gavenski, Lucas S. Kupssinskü, Adilson Medronha, Luis V. Moura, Gabriel S. Simões, and Rodrigo C. Barros · 2023
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Evaluating the social impact of generative ai systems in systems and society
Irene Solaiman, Zeerak Talat, William Agnew, Lama Ahmad, Dylan Baker, Su Lin Blodgett, Hal Daumé III, Jesse Dodge, Ellie Evans, Sara Hooker, et al · 2023
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zkdl: Efficient zero-knowledge proofs of deep learning training
Hao-Lun Sun and Hongyang Zhang · 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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Privacy-preserving and trustless verifiable fairness audit of machine learning models
Gui Tang, Wuzheng Tan, and Mei Cai · 2023
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Accelerating the PlonK zkSNARK Proving System using GPU Architectures
Tal Derei · 2023
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cqlin: Efficient linear operations on kzg commitments with cached quotients
Liam Eagen and Ariel Gabizon · 2023
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Tensorplonk: A “gpu”’ for zkml, delivering 1,000x speedups
Daniel D. Kang · 2023
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Deepreshape: Redesigning neural networks for efficient private inference
Nandan Kumar Jha and Brandon Reagen · 2023
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zkllm: Zero knowledge proofs for large language models
Haochen Sun, Jason Li, and Hongyang Zhang · 2024
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Zkml: An optimizing system for ml inference in zero-knowledge proofs
Bing-Jyue Chen, Suppakit Waiwitlikhit, Ion Stoica, and Daniel Kang · 2024
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A survey on evaluation of large language models
Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, et al · 2024
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Powers-of-tau to the people: Decentralizing setup ceremonies
Valeria Nikolaenko, Sam Ragsdale, Joseph Bonneau, and Dan Boneh · 2024
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Trust the process: Zero-knowledge machine learning to enhance trust in generative ai interactions
Bianca-Mihaela Ganescu and Jonathan Passerat-Palmbach · 2024
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