Fetching the paper…
Reading the bibliography…
Systems that offer continuous model monitoring have emerged in response to (1) well-documented failures of deployed Machine Learning (ML) and Artificial Intelligence (AI) models and (2) new regulatory requirements impacting these models.
ML Health: Fitness Tracking for Production Models
Sindhu Ghanta, Sriram Subramanian, Lior Khermosh, Swaminathan Sundararaman, Harshil Shah, Yakov Goldberg, Drew S. Roselli, and Nisha Talagala. 2019 · 1902
Earlier work this paper cites.
Uniform guidelines on employee selection procedures
Equal Employment Opportunity Commission, Civil Service Commission, et al. 1978 · 1978
Earlier work this paper cites.
Divergence measures based on the Shannon entropy
Jianhua Lin. 1991 · 1991
Earlier work this paper cites.
Tolerating concept and sampling shift in lazy learning using prediction error context switching
Marcos Salganicoff. 1997 · 1997
Earlier work this paper cites.
Learning concept drift with a committee of decision trees
Kenneth O Stanley. 2003 · 2003
Earlier work this paper cites.
Achieving Fairness via Post-Processing in Web-Scale Recommender Systems
Preetam Nandy, Cyrus Diciccio, Divya Venugopalan, Heloise Logan, Kinjal Basu, and Noureddine El Karoui. 2021 · 2006
Earlier work this paper cites.
Learning from time-changing data with adaptive windowing. In Proceedings of the 2007 SIAM international conference on data mining . SIAM, 443–448
Albert Bifet and Ricard Gavalda. 2007 · 2007
Earlier work this paper cites.
Detecting sudden concept drift with knowledge of human behavior. In 2008 IEEE International Conference on Systems, Man and Cybernetics . 3261–3267
K. Nishida, S. Shimada, S. Ishikawa, and K. Yamauchi. 2008 · 2008
Earlier work this paper cites.
The wasserstein distances
Cédric Villani. 2009 · 2009
Earlier work this paper cites.
Indre Žliobaite. 2010 · 2010
Earlier work this paper cites.
Fairness through awareness. In Proceedings of the 3rd innovations in theoretical computer science conference . 214–226
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. 2012 · 2012
Earlier work this paper cites.
Data preprocessing techniques for classification without discrimination
Faisal Kamiran and Toon Calders. 2012 · 2012
Earlier work this paper cites.
On evaluating stream learning algorithms
Joao Gama, Raquel Sebastiao, and Pedro Pereira Rodrigues. 2013 · 2013
Earlier work this paper cites.
Hidden technical debt in machine learning systems
David Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-Francois Crespo, and Dan Dennison. 2015 · 2015
Earlier work this paper cites.
Layer-wise relevance propagation for neural networks with local renormalization layers. In International Conference on Artificial Neural Networks . Springer, 63–71
Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, Klaus-Robert Müller, and Wojciech Samek. 2016 · 2016
Earlier work this paper cites.
Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems. In 2016 IEEE symposium on security and privacy (SP) . IEEE, 598–617
Anupam Datta, Shayak Sen, and Yair Zick. 2016 · 2016
Earlier work this paper cites.
Fast unsupervised online drift detection using incremental kolmogorov-smirnov test. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . 1545–1554
Denis Moreira dos Reis, Peter Flach, Stan Matwin, and Gustavo Batista. 2016 · 2016
Earlier work this paper cites.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nathan Srebro. 2016 · 2016
Earlier work this paper cites.
Inherent trade-offs in the fair determination of risk scores
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan. 2016 · 2016
Earlier work this paper cites.
" Why should i trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining . 1135–1144
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Earlier work this paper cites.
Algorithmic decision making and the cost of fairness. In Proceedings of the 23rd acm sigkdd international conference on knowledge discovery and data mining . 797–806
Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, and Aziz Huq. 2017 · 2017
Cited alongside, same era.
Matt J Kusner, Joshua R Loftus, Chris Russell, and Ricardo Silva. 2017 · 2017
Cited alongside, same era.
A Unified Approach to Interpreting Model Predictions
Scott M Lundberg and Su-In Lee. 2017 · 2017
Cited alongside, same era.
Learning important features through propagating activation differences. In International conference on machine learning . PMLR, 3145–3153
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje. 2017 · 2017
Cited alongside, same era.
Axiomatic attribution for deep networks. In International Conference on Machine Learning . PMLR, 3319–3328
Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017 · 2017
Explainable machine learning in deployment. In Proceedings of the 2020 conference on fairness, accountability, and transparency . 648–657
Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José MF Moura, and Peter Eckersley. 2020 · 2020
Later among the works it cites.
Fliptest: fairness testing via optimal transport. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency . 111–121
Emily Black, Samuel Yeom, and Matt Fredrikson. 2020 · 2020
Later among the works it cites.
Wasserstein fair classification. In Uncertainty in Artificial Intelligence . PMLR, 862–872
Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, and Silvia Chiappa. 2020 · 2020
Later among the works it cites.
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
Amir-Hossein Karimi, Gilles Barthe, Bernhard Schölkopf, and Isabel Valera. 2020 · 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.
Rachel KE Bellamy, Kuntal Dey, Michael Hind, Samuel C Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, et al · 2018
Cited alongside, same era.
Help wanted: An examination of hiring algorithms, equity, and bias
Miranda Bogen and Aaron Rieke. 2018 · 2018
Cited alongside, same era.
The measure and mismeasure of fairness: A critical review of fair machine learning
Sam Corbett-Davies and Sharad Goel. 2018 · 2018
Cited alongside, same era.
Delayed impact of fair machine learning. In International Conference on Machine Learning . PMLR, 3150–3158
Lydia T Liu, Sarah Dean, Esther Rolf, Max Simchowitz, and Moritz Hardt. 2018 · 2018
Cited alongside, same era.
On challenges in machine learning model management
Sebastian Schelter, Felix Biessmann, Tim Januschowski, David Salinas, Stephan Seufert, and Gyuri Szarvas. 2018 · 2018
Cited alongside, same era.
“Meaningful Information” and the Right to Explanation. In Conference on Fairness, Accountability and Transparency . PMLR, 48–48
Andrew Selbst and Julia Powles. 2018 · 2018
Cited alongside, same era.
H.R.2231 - Algorithmic Accountability Act of 2019
116th Congress (2019-2020). [n. d.] · 2019
Cited alongside, same era.
Niki Kilbertus, Philip J Ball, Matt J Kusner, Adrian Weller, and Ricardo Silva. 2020 · 2020
Later among the works it cites.
The Explanation Game: Explaining Machine Learning Models Using Shapley Values. In International Cross-Domain Conference for Machine Learning and Knowledge Extraction . Springer, 17–38
Luke Merrick and Ankur Taly. 2020 · 2020
Later among the works it cites.
Wasserstein-based fairness interpretability framework for machine learning models
Alexey Miroshnikov, Konstandinos Kotsiopoulos, Ryan Franks, and Arjun Ravi Kannan. 2020 · 2020
Later among the works it cites.
Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices. In Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAT*)
Manish Raghavan, Solon Barocas, Jon Kleinberg, and Karen Levy. 2020 · 2020
Later among the works it cites.
Bias preservation in machine learning: the legality of fairness metrics under EU non-discrimination law
Sandra Wachter, Brent Mittelstadt, and Chris Russell. 2020 · 2020
Later among the works it cites.
Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs
Solon Barocas, Anhong Guo, Ece Kamar, Jacquelyn Krones, Meredith Ringel Morris, Jennifer Wortman Vaughan, Duncan Wadsworth, and Hanna Wallach. 2021 · 2021
Closest in time.
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021 · 2021
Closest in time.
Fairness Measures for Machine Learning in Finance
Sanjiv Das, Michele Donini, Jason Gelman, Kevin Haas, Mila Hardt, Jared Katzman, Krishnaram Kenthapadi, Pedro Larroy, Pinar Yilmaz, and Muhammad Bilal Zafar. 2021 · 2021
Closest in time.
Characterizing Intersectional Group Fairness with Worst-Case Comparisons
Avijit Ghosh, Lea Genuit, and Mary Reagan. 2021 · 2021
Closest in time.
Moving Towards Responsible Government Use of AI in New Zealand)
Alistair Knott. [n. d.] · 2021
Closest in time.
The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies
Aniek F Markus, Jan A Kors, and Peter R Rijnbeek. 2021 · 2021
Closest in time.
David Nigenda, Zohar Karnin, Muhammad Bilal Zafar, Raghu Ramesha, Alan Tan, Michele Donini, and Krishnaram Kenthapadi. 2021 · 2021
Closest in time.
Unified Shapley Framework to Explain Prediction Drift
Aalok Shanbhag, Avijit Ghosh, and Josh Rubin. 2021 · 2021
Closest in time.
Building and auditing fair algorithms: A case study in candidate screening. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency . 666–677
Christo Wilson, Avijit Ghosh, Shan Jiang, Alan Mislove, Lewis Baker, Janelle Szary, Kelly Trindel, and Frida Polli. 2021 · 2021
Closest in time.
Best Tools to Do ML Model Monitoring
Jakub Czakon. 2022 · 2022
Closest in time.